Cellular Mechanics and Biophysics: Structure and Function of Basic Cellular Components Regulating Cell Mechanics [1st ed.] 9783030585310, 9783030585327

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Biological and Medical Physics, Biomedical Engineering

Claudia Tanja Mierke

Cellular Mechanics and Biophysics Structure and Function of Basic Cellular Components Regulating Cell Mechanics

Biological and Medical Physics, Biomedical Engineering

BIOLOGICAL AND MEDICAL PHYSICS, BIOMEDICAL ENGINEERING This series is intended to be comprehensive, covering a broad range of topics important to the study of the physical, chemical and biological sciences. Its goal is to provide scientists and engineers with textbooks, monographs, and reference works to address the growing need for information. The fields of biological and medical physics and biomedical engineering are broad, multidisciplinary and dynamic. They lie at the crossroads of frontier research in physics, biology, chemistry, and medicine. Books in the series emphasize established and emergent areas of science including molecular, membrane, and mathematical biophysics; photosynthetic energy harvesting and conversion; information processing; physical principles of genetics; sensory communications; automata networks, neural networks, and cellular automata. Equally important is coverage of applied aspects of biological and medical physics and biomedical engineering such as molecular electronic components and devices, biosensors, medicine, imaging, physical principles of renewable energy production, advanced prostheses, and environmental control and engineering.

Editor-in-Chief Bernard S. Gerstman, Department of Physics, Florida International University, Miami, FL, USA

Series Editors Masuo Aizawa, Tokyo Institute Technology, Tokyo, Japan Robert H. Austin, Princeton, NJ, USA

Xiang Yang Liu, Department of Physics, Faculty of Sciences, National University of Singapore, Singapore, Singapore

James Barber, Wolfson Laboratories, Imperial College of Science Technology, London, UK

David Mauzerall, Rockefeller University, New York, NY, USA

Howard C. Berg, Cambridge, MA, USA

Eugenie V. Mielczarek, Department of Physics and Astronomy, George Mason University, Fairfax, USA

Robert Callender, Department of Biochemistry, Albert Einstein College of Medicine, Bronx, NY, USA George Feher, Department of Physics, University of California, San Diego, La Jolla, CA, USA Hans Frauenfelder, Los Alamos, NM, USA Ivar Giaever, Rensselaer Polytechnic Institute, Troy, NY, USA Pierre Joliot, Institute de Biologie Physico-Chimique, Fondation Edmond de Rothschild, Paris, France Lajos Keszthelyi, Biological Research Center, Hungarian Academy of Sciences, Szeged, Hungary

Markolf Niemz, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany V. Adrian Parsegian, Physical Science Laboratory, National Institutes of Health, Bethesda, MD, USA Linda S. Powers, University of Arizona, Tucson, AZ, USA Earl W. Prohofsky, Department of Physics, Purdue University, West Lafayette, IN, USA Tatiana K. Rostovtseva, NICHD, National Institutes of Health, Bethesda, MD, USA Andrew Rubin, Department of Biophysics, Moscow State University, Moscow, Russia

Paul W. King, Biosciences Center and Photobiology, National Renewable Energy Laboratory, Lakewood, CO, USA

Michael Seibert, National Renewable Energy Laboratory, Golden, CO, USA

Gianluca Lazzi, University of Utah, Salt Lake City, UT, USA

Nongjian Tao, Biodesign Center for Bioelectronics, Arizona State University, Tempe, AZ, USA

Aaron Lewis, Department of Applied Physics, Hebrew University, Jerusalem, Israel

David Thomas, Department of Biochemistry, University of Minnesota Medical School, Minneapolis, MN, USA

Stuart M. Lindsay, Department of Physics and Astronomy, Arizona State University, Tempe, AZ, USA

More information about this series at http://www.springer.com/series/3740

Claudia Tanja Mierke

Cellular Mechanics and Biophysics Structure and Function of Basic Cellular Components Regulating Cell Mechanics

123

Claudia Tanja Mierke Faculty of Physics and Earth Sciences, Peter Debye Institute for Soft Matter Physics, Biological Physics University of Leipzig Leipzig, Germany

ISSN 1618-7210 ISSN 2197-5647 (electronic) Biological and Medical Physics, Biomedical Engineering ISBN 978-3-030-58531-0 ISBN 978-3-030-58532-7 (eBook) https://doi.org/10.1007/978-3-030-58532-7 © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

The Cellular Mechanics and Biophysics book is completed by the great support and encouragement of Thomas M. L. Mierke.

Preface

Dear Reader My Cellular Mechanics and Biophysics book covers the novel and promising field of the analysis of the mechanical properties of cells and cell components and its connection to cell and molecular biological research. Therefore, basic knowledge about cellular structures and processes is recommended to combine them with mechanical features. Beyond that, cell mechanics has become a focus of physical and biological research all over the world. The scope of the Cellular Mechanics and Biophysics book is to present molecular, cellular and biochemical principles and structures and to explain several biophysical approaches used in cellular biophysics. My book presents the basic concepts and processes of biological matter and major findings that contribute significantly to the multidisciplinary field of Cellular Mechanics and Biophysics from a biophysical point of view. The dedicated reader is everyone interested in biophysical research focusing on cells such as readers who study biological physics, physics, biotechnology, life science, biomedicine or biology or work in those fields. The book is suited for upper undergraduate, graduate, doctoral and post-doctoral students, senior scientists, lecturers, principal investigators and professors. I hope that the Cellular Mechanics and Biophysics book will serve as a standard work for all those interested in this field of research. To my knowledge, there is no standard work in English, neither as hardcover nor as e-book, which can neither serve as standard work in the field of Cellular Mechanics and Biophysics nor serve to gain deeper knowledge about this highly interesting field of research. Many parts of the book are illustrated by self-designed figures that can be used in future lectures or seminars. In my own lectures, in which I teach physics students, I can use this book as a reference guide and recommend it as further reading, as it covers all aspects and topics. In any case, this book is also suitable for reading in addition to the lectures and internships, which are intended to help students and scientists gain insights into the field of Cellular Mechanics and Biophysics. This book can even serve as a standard work in this field. The main reason for covering all these different topics, which relate to several disciplines, has been to write a large book consisting that help the reader to understand the general mechanisms and principles of biological matter from a biophysical point of view. However, the Cellular Mechanics and Biophysics book is for all those who are interested in the field of cellular biomechanics and biological physics. I hope that the book will help to vii

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Preface

understand why Cellular Mechanics and Biophysics are so important for soft matter and biological research. In summary, all readers, such as students and researchers, should be guided by the book through the broad field of Cellular Mechanics and Biophysics. Each chapter of the 17 chapters is a closed part containing a large number of cited references that can serve as further reading material. Finally, the book will contribute to establishing Cellular Mechanics and Biophysics as an essential part of biological soft matter research.

Leipzig, Germany

Sincerely Claudia Tanja Mierke

Contents

Part I

1

Introduction into Biophysics and Basic Biological Processes from a Physical Point-of-view

The Definition of Biophysics: What Exactly is Biophysics? . . . 1.1 Introduction to Biophysics . . . . . . . . . . . . . . . . . . . . . . . 1.2 Historical Overview of Selected Early Developments in Biophysics with a Focus on Cell Mechanics . . . . . . . . 1.2.1 Isolated Protoplasmic Mechanics . . . . . . . . . . . 1.2.2 Future View on Cell Mechanics. . . . . . . . . . . . 1.3 Major Fields of Biophysics . . . . . . . . . . . . . . . . . . . . . . . 1.4 Molecular Biophysics . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.1 Subdiscipline: Structural Molecular Biophysics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.2 Subdiscipline: Nanotechnology . . . . . . . . . . . . 1.5 Systems Biophysics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.1 Subdiscipline: Immunological Systems Biophysics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.2 Subdiscipline: Developmental and Evolutionary Systems Biophysics . . . . . . . . . . 1.6 Biophysics-Related Discipline: Biophysical Chemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7 Cellular Biophysics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7.1 Subdiscipline: Membrane Cellular Biophysics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7.2 Subdiscipline: Nuclear Cellular Biophysics . . . 1.8 Cellular Mechanics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.9 Biophysical Bioengineering of the Cellular Microenvironment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.10 “Experimental” Biophysics is Guided by Theoretical Biophysics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.11 Short Introduction into Biophysical Models Quantifying Cell Mechanics . . . . . . . . . . . . . . . . . . . . . .

3 3 7 8 11 14 15 15 16 16 18 18 19 20 21 21 22 24 25 25

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1.12

Why Are These Quantitative Models Usually Idealized? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.12.1 What is the Conceptual Framework? . . . . . . . . 1.12.2 Unifying Ideas of Biological Processes . . . . . . 1.12.3 What Are the Unifying Ideas of Biology? . . . . 1.12.4 Why Do We Need Mathematics? . . . . . . . . . . . 1.12.5 What Role Play Numbers in Cell Mechanics and Biophysics? . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2

3

4

Focus 2.1 2.2 2.3

on Eukaryotic Cells . . . . . . . . . . . . . . . . . . . . . . . . What Is Biological Soft Matter in General? . . . . . . Four Major Classes of Macromolecules . . . . . . . . . Why Are Physical Models Needed and Kept Simple? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Characteristics of Prokaryotes and Eukaryotes . . . . 2.4.1 Prokaryotic Cells . . . . . . . . . . . . . . . . . . . 2.4.2 Eukaryotic Cells . . . . . . . . . . . . . . . . . . . . 2.5 Organelles—What Defines Them? . . . . . . . . . . . . . 2.6 Endosymbiotic Hypothesis . . . . . . . . . . . . . . . . . . . 2.7 Single or Serial Endosymbiosis . . . . . . . . . . . . . . . . 2.8 How Can Eukaryotes Lose Their Mitochondria? . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

28 29

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35 35 36

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38 43 43 44 45 47 50 53 54

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Biomechanical View on the Cytoplasm (and Cytosol) of Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Biomechanical Picture of the Cytoplasm . . . . . . . . . . . . . 3.1.1 Cells Serve as an Example for Engineered Designs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.2 Cells Are Treated as Gels . . . . . . . . . . . . . . . . 3.1.3 Dynamics of Cells . . . . . . . . . . . . . . . . . . . . . . 3.1.4 Cells, Gels and Motion . . . . . . . . . . . . . . . . . . 3.2 Anisotropic Mechanics and Dynamics of the Cytoplasm of Living Cells . . . . . . . . . . . . . . . . . . . . . . . 3.3 Relation Between the Shape of the Nucleus and Cytoskeletal Mechanics . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Cytoplasm Mechanics Are Size- and VelocityDependent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Focal Adhesion Proteins Regulate Cell–Matrix and Cell–Cell Adhesion and Act as Force Sensors . . . . . 4.1 Introduction to Cell–Matrix Adhesion . . . . . . . . . . . 4.1.1 The Major Adhesion Types . . . . . . . . . . . 4.1.2 Focal Point Contacts . . . . . . . . . . . . . . . . 4.1.3 Focal Adhesions. . . . . . . . . . . . . . . . . . . . 4.1.4 Fibrillar Adhesions . . . . . . . . . . . . . . . . . . 4.2 Focal Adhesions and Focal Adhesion Proteins . . . . 4.2.1 Vinculin and Interacting Proteins . . . . . . .

26 26 27 27 28

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57 57 59 59 63 64 65 69 80 87 95 95 95 96 96 96 97 98

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5

4.2.2 Activation of Vinculin . . . . . . . . . . . . . . . . . . . 4.2.3 Role of Vinculin in Cell–Matrix Adhesions . . . 4.3 Transmission and Generation of Cellular Traction Forces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 Breakdown of Cell Adhesions at the Rear End . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.2 Interaction of Vinculin and Actin in Focal Adhesions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.3 Vinculin Regulation in Cell–Matrix Adhesions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.4 Phosphorylation of Vinculin . . . . . . . . . . . . . . 4.3.5 Interaction of Vinculin and PIP2 . . . . . . . . . . . 4.4 Vinculin Functions in Cell–Cell Adherence Junctions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Unanswered Questions Regarding Vinculin for Future Investigations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 Mechanotransduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6.1 Besides Functioning as a Passive Supporter for Cells, the Extracellular Matrix Can Fulfill Additional Tasks . . . . . . . . . . . . . . . . . . . . . . . 4.6.2 Mechanisms of Mechanotransduction. . . . . . . . 4.7 Integrin-Associated Complex-Facilitated Mechanotransduction Causes Transcription and Differentiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.8 Force Sensors Serve as Mechanical Markers . . . . . . . . . . 4.8.1 Force Sensor . . . . . . . . . . . . . . . . . . . . . . . . . . 4.8.2 Besides Vinculin, Talin Acts as a Force Sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.9 The Lamellipodium Acts as an Alternative Force Sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.9.1 The Spread Area Results from a Biphasic Response of the Stiffness of the Substrate Which Is Independent of Myosin Activity . . . . 4.9.2 Dynamic Remodeling of Lamellipodia Is Not Altered by the Material Stiffness . . . . . . . . . . . 4.9.3 Mn2+-Based Integrin Activation Induces Cell Spreading on Soft Substrates . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

98 100

Structure and Function of the Mitochondrion . . . . . . . . . 5.1 Introduction to the Mitochondrion. . . . . . . . . . . . . . 5.2 Mitochondrial Shape and Function in Energy Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Mitochondrial Chromosome . . . . . . . . . . . . . . . . . . 5.4 Segregation Modes of the mtDNA . . . . . . . . . . . . . 5.5 Dynamin-Facilitated Mitochondrial Dynamics . . . . . 5.6 Endoplasmatic Reticulum-Driven Division of the Mitochondrion . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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141 141

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142 144 145 146

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102 103 103 104 104 105 105 107 108

109 111

116 118 119 120 122

124 125 125 129

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5.7 5.8 5.9 5.10 5.11

6

What Determines the Division of the Mitochondria?. . . . Role of the ERMD Microdomain . . . . . . . . . . . . . . . . . . Coordination of Various Mitochondrial Behaviors . . . . . How are Mitochondrial Pathways Controlled? . . . . . . . . Controversial Discussion of the Drp1/Dnm1Independent Mitophagy . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

148 149 150 151

Mechanical View on the Mitochondria . . . . . . . . . . . . . . . . . . 6.1 Mechanical Factors Regulate the Structure and Function of Mitochondria . . . . . . . . . . . . . . . . . . . . . . . . 6.2 How Are Mitochondrial Structure and Production of ATP Related? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Cluster Formation of Mitochondria . . . . . . . . . . . . . . . . . 6.4 Properties of Mitochondrial Networks . . . . . . . . . . . . . . . 6.5 Interactions Between the Cytoskeleton and Mitochondria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.1 Interactions Between Mitochondria and the Actin Cytoskeleton. . . . . . . . . . . . . . . . . . . . . . 6.5.2 Can Microtubules Impact the Function of Mitochondria? . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.3 How Can Intermediate Filaments Affect Function and Structure of Mitochondria? . . . . . 6.6 Mechanobiological Aspects of the Structure and Function of Mitochondria . . . . . . . . . . . . . . . . . . . . . . . . 6.7 Impact of Transient and Monotonous Stretching . . . . . . . 6.8 Fluctuations Affect Mechanotransduction Behavior . . . . . 6.9 Role of the Stiffness of the Extracellular Matrix on Mitochondria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.10 How Contributes the Mitochondrial Structure and Function to Diseases and Aging? . . . . . . . . . . . . . . . . . . 6.11 Mechanical Force Evokes Mitochondrial Fission . . . . . . 6.11.1 Mitochondria Are Affected by the ActinDriven Movement of Shigella Flexneri . . . . . . 6.11.2 The Fission of Mitochondria Can Be Triggered with an Atomic Force Microscope . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.11.3 Mitochondrial Cleavage of Cells Placed on Patterned Substrates . . . . . . . . . . . . . . . . . . . . . 6.12 The Inhibition of the ER or the Dynamics of Actin May Not Significantly Alter the Force-Induced Fission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.13 Force-Sensing Mechanism and the Recruitment of DRP1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

163

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163 165 165 168 170 171 172 172 172 173 174 175 176 177 177

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7

Mechanical View on the Endoplasmatic Reticulum and Golgi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Introduction to the Endoplasmic Reticulum (ER) . . . . . . 7.2 The Structure of the ER . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 The Shaping of ER Proteins . . . . . . . . . . . . . . . . . . . . . . 7.3.1 ER Tubules . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.2 ER Sheets . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4 The Synthesis and Folding of Proteins in the ER . . . . . . 7.4.1 The Biogenesis of Lipids . . . . . . . . . . . . . . . . . 7.4.2 The Metabolism of Calcium (Ca2+) . . . . . . . . . 7.5 How Is the Shape and the Function of the ER Regulated? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6 The Effect on Stress on the ER . . . . . . . . . . . . . . . . . . . . 7.7 The General Structure and Function of Golgi Apparatus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.7.1 Why Are There Various Classes of Golgi Cisternae? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.7.2 Organization of the Golgi and Models for Golgi-Based Trafficking . . . . . . . . . . . . . . . . . . 7.7.3 Organization of the Golgi with the Positioning of Resident Proteins . . . . . . . . . . . 7.7.4 Organized Golgi and the Trafficking System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.7.5 The Overall Maturation Versus Micromaturation . . . . . . . . . . . . . . . . . . . . . . . . 7.8 Golgi Apparatus and Vesicle-Based Protein Trafficking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.8.1 How Are Proteins Altered When They Travel Through the Golgi Apparatus? . . . . . . . 7.8.2 The Cisternal Maturation Model . . . . . . . . . . . 7.8.3 Which Model Is Best Suited for the Precise Description of the Golgi Functions?. . . . . . . . . 7.8.4 What Are the Major Questions About the Trafficking of Proteins Through the Golgi Apparatus? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.9 The Interface Between Endoplasmatic Reticulum and Golgi Serves for Protein Sorting . . . . . . . . . . . . . . . . . . . 7.9.1 What Are the Underlying Principles of Selective Capture by Vesicles? . . . . . . . . . . . . 7.9.2 Recognition of Sorting Signal by Coat Adaptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.9.3 Coupling of Cargo and Coat by Cargo Receptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.9.4 Sorting Mechanisms at the ER–Golgi Connection Site . . . . . . . . . . . . . . . . . . . . . . . . 7.10 What Mechanistic Roles Fulfill Cargo Receptors in Sorting Processes? . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.10.1 Cargo Receptors Provide ER Export . . . . . . . .

191 191 192 194 194 197 198 200 200 202 202 204 205 207 208 208 211 212 213 214 215

216 217 217 218 218 219 221 221

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7.10.2

Cargo Receptors Act in the Retrograde COPI Trafficking . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.10.3 Regulatory Processes of Cargo Sorting and Impact on Trafficking by the Cargo . . . . . . . . . 7.10.4 Large Carriers Represent a Specialized ER Export Mechanism . . . . . . . . . . . . . . . . . . . . . . 7.10.5 Cargo-Driven Signaling into the cis-Golgi . . . . 7.10.6 How Is the Process of Sorting Dynamically Regulated? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.11 Ribosomes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.11.1 Ribosomes Are Transported via the Nuclear Transport System . . . . . . . . . . . . . . . . . . . . . . . 7.11.2 What Function Fulfills the Ribosome? . . . . . . . 7.12 Ribosome Mechanics Sheds Light on the Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.12.1 Physical Models Can Be Employed for Elastic Networks . . . . . . . . . . . . . . . . . . . . . . . 7.12.2 Ratchet Motion Within a Complex of Three tRNAs and One mRNA . . . . . . . . . . . . . . . . . . 7.12.3 Complex Motions at an Early Decoding Stage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.12.4 The mRNA Helicase Function of the Ribosome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.13 ER Mechanics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.13.1 The Fit of the Fibers and Analysis of Fiber Mechanics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.13.2 How Can ER Tubules Be Monitored? . . . . . . . 7.14 Golgi Mechanics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Mechanical View on Vacuoles. . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Introduction to Vacuoles . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Volume Regulation Mechanism in Vacuoles . . . . . . . . . . 8.2.1 The Increasing Vacuole Content Activates SNAREs to Enlarge the Volume of the Organelle Volume by Inducing Their Homotypic Fusion . . . . . . . . . . . . . . . . . . . . . . 8.2.2 The Fusion of Homotypic Vacuoles Requires Polyphosphate Synthesis and Accumulation . . . . . . . . . . . . . . . . . . . . . . 8.2.3 The Fusion of Vacuoles is not Altered by the Cytosolic Polyphosphate Concentration . . . . . . 8.2.4 In the Absence of Polyphosphate Synthesis, the Priming Step Remains the Only Fusion Step that is Interrupted . . . . . . . . . . . . . . . . . . .

223 224 224 225 225 226 227 228 229 231 233 233 234 236 238 240 244 246 263 263 264

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8.2.5

Functional Interaction of the Vacuolar SNARE Nyv1 and the Cyclin Pho80 with the VTC Complex . . . . . . . . . . . . . . . . . . . . . . . . . 269 8.3 Mechanics of Vacuoles . . . . . . . . . . . . . . . . . . . . . . . . . . 272 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 Lysosomes and Peroxisomes . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1 Structure and Function of Lysosomes . . . . . . . . . . . . . . . 9.1.1 Fusion and Reformation of Lysosomes . . . . . . 9.1.2 Newly Synthesized Proteins Are Targeted to Lysosomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1.3 Secretory Lysosomes . . . . . . . . . . . . . . . . . . . . 9.1.4 How Travel Secretory Proteins to Lysosomes? . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1.5 How Are the Final Stages of the Secretion Regulated? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Lysosomes Can Respond to Environment Mechanics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3 Structure and Function of Peroxisomes . . . . . . . . . . . . . . 9.3.1 Functions of Peroxisomes in Metabolism . . . . . 9.3.2 Heterogeneity of Peroxisomes . . . . . . . . . . . . . 9.3.3 Peroxins, Import of Proteins and Membrane Adaptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.4 Peroxisomal Membrane Proteins . . . . . . . . . . . 9.3.5 The Assembly of the Peroxisome . . . . . . . . . . 9.3.6 Division of Peroxisomes . . . . . . . . . . . . . . . . . 9.3.7 Motility and Distribution of Peroxisomes. . . . . 9.3.8 Tether-Based Interactions of the Peroxisomes . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.9 Signal Transduction in Peroxisomes and Defense Mechanisms for Viruses . . . . . . . . . . . 9.4 Interplay Between Peroxisomes and Cancer . . . . . . . . . . 9.5 Intracellular Mechanics and Peroxisomes . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

277 277 280

10 The Cell Nucleus and Its Compartments . . . . . . . . . . . . . . . . . 10.1 Cell Nucleus: Architecture and Function . . . . . . . . . . . . . 10.1.1 Nuclear Shape and the Mechanical Stability of the Nucleus . . . . . . . . . . . . . . . . . . . . . . . . . 10.1.2 Quantification of Elasticity and Viscosity of the Plasma Membrane, Cytoskeleton and Nucleus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1.3 Viscoelastic Model Based on the K-V Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1.4 The Propagation of the Error Propagation in This Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1.5 The Cytoskeleton Decreases the Process of the Strain Recovery in the Nucleus . . . . . . .

333 333

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282 284 285 288 290 293 298 300 301 302 303 304 306 307 309 311 315 316

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10.1.6

10.2 10.3 10.4

10.5 10.6

10.7

Cancer Cells from a Later Disease Stage Are Characterized by a Lower Elastic Modulus, Decreased Viscosity and Smaller Time Constant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Sites of Transcription and Splicing Factor Compartments (SFCs) . . . . . . . . . . . . . . . . . . . . . . . . . . . The Nucleolus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Cajal Bodies and PML Bodies . . . . . . . . . . . . . . . . . 10.4.1 The Cajal Bodies . . . . . . . . . . . . . . . . . . . . . . . 10.4.2 PML Bodies . . . . . . . . . . . . . . . . . . . . . . . . . . . Nuclear Envelope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Function of the Nuclear Pore Complex . . . . . . . . . . . . . . 10.6.1 The Ultrastructure of the Nuclear Pore Complex Reveals a Three Stacked Ring Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.6.2 The Module-Based Assembly of the Nuclear Pore Complex . . . . . . . . . . . . . . . . . . . . . . . . . 10.6.3 Dynamics and Stable Elements Built the Nuclear Pore Complex . . . . . . . . . . . . . . . . . . . 10.6.4 A Few Architectural Elements Form the Nuclear Pore Complex . . . . . . . . . . . . . . . . . . . 10.6.5 Evolutionary Conserved Nuclear Pore Complex Structure . . . . . . . . . . . . . . . . . . . . . . 10.6.6 How Can the Architecture Determine the Function? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.6.7 Is There a Functional Overlap Between Nups and NTRs? . . . . . . . . . . . . . . . . . . . . . . . . . . . . Transport System of the Nucleus . . . . . . . . . . . . . . . . . . 10.7.1 Nuclear Pore Complex (NPC) . . . . . . . . . . . . . 10.7.2 The Structural Assembly of the Nuclear Pore Complex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.7.3 Transport Through the Nuclear Pore Complex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.7.4 The Permeability Barrier of the Nuclear Pore Complex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.7.5 Variants of Nuclear Pore Complexes . . . . . . . . 10.7.6 Protein Translocation to the Inner Nuclear Membrane (INM) . . . . . . . . . . . . . . . . . . . . . . . 10.7.7 Roles of the Nuclear Envelope (NE), Lamins and LINC Complex . . . . . . . . . . . . . . . . . . . . . 10.7.8 Processing, Assembly and Export of Messenger RNA. . . . . . . . . . . . . . . . . . . . . . . . 10.7.9 Does a Transcriptional Control at the Nuclear Pore Complex Exist? . . . . . . . . . . . . . 10.7.10 Are There Connections Between Nucleocytoplasmic Transport and Human Diseases? . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

346 347 347 350 350 352 353 355

356 356 357 358 359 360 362 363 364 364 365 366 368 369 370 371 373

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10.7.11 What Are the Future Directions of Nuclear Transport Mechanisms? . . . . . . . . . . . . . . . . . . 10.8 Nuclear Plasma and Cytoskeleton . . . . . . . . . . . . . . . . . . 10.9 The LINC Complex . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.10 Chromosomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.10.1 The Structure of Chromosomes . . . . . . . . . . . . 10.10.2 Chromosomes and Nuclear Compartmentalization . . . . . . . . . . . . . . . . . . . 10.10.3 Dynamics of the Nucleus: Chromatin Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.10.4 How Affects the Chromatin the Assembly of the Nuclear Envelope? . . . . . . . . . . . . . . . . . 10.10.5 Dynamics of the Nucleus: RNA Dynamics . . . 10.10.6 Dynamics of the Nucleus: Protein Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.10.7 Dynamics of the Nucleus: Compartment Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.11 Nuclear Mechanics and Impact of Perturbations and Fluctuations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Part II

375 376 377 379 380 380 381 383 384 385 386 388 394

Fundamental Cellular Processes and Their Connection to Mechanics

11 Genetic Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Introduction into the Genetic Code . . . . . . . . . . . . . . . . . 11.2 Tessera Model for the Evolution of the Genetic Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2.1 Symmetry and Degeneracy . . . . . . . . . . . . . . . 11.2.2 Evolutionary Implications . . . . . . . . . . . . . . . . 11.3 Historical View on the Genetic Code Deciphering . . . . . 11.4 The Standard Code, Variations and the Latest Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.5 Evolutions of the Genetic Code, Such as Old and Novel Amino Acids . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.6 Why Is the Genetic Code Usually Universal? Are There Exceptions? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.7 Theories of the Code Origin and Evolution . . . . . . . . . . 11.7.1 The Stereochemical Theory . . . . . . . . . . . . . . . 11.7.2 The Co-evolution Theory (or Synonymously Metabolic Theory) . . . . . . . . . . . . . . . . . . . . . . 11.7.3 The Error Minimization Theory . . . . . . . . . . . . 11.8 Does a Co-evolution of the Genetic Code and the Translational Process Exist? . . . . . . . . . . . . . . . . . . . . . . 11.9 Rewriting the Genetic Code: The Concept of Code Expansion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.9.1 The Expansion of the Genetic Code . . . . . . . .

417 417 420 420 424 431 435 436 437 438 438 440 440 443 445 446

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11.9.2

How Can Orthogonal tRNAs Be Expressed in Eukaryotic Cells? . . . . . . . . . . . . . . . . . . . . . 11.9.3 Uaa-Specific Synthetase Generation for Mammalian Cells . . . . . . . . . . . . . . . . . . . . . . . 11.9.4 The aaRS Recognition of tRNACUA Can Be Improved . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.9.5 The Uaa Specificity of aaRS Can Be Widened. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.9.6 Can the Bioavailability of Uaa Be Enhanced? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.9.7 The Inactivation of NMD Stabilizes UAG-Containing mRNA . . . . . . . . . . . . . . . . . 11.9.8 Knock-Out of RF1 to Enhance the Insertion of Uaa Incorporation at Multiple Locations . . . . 11.9.9 Transfer of the Uaa Techniques to Stem Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.10 Non-power Model of the Genetic Code . . . . . . . . . . . . . 11.10.1 Euplotid Nuclear Genetic Code . . . . . . . . . . . . 11.10.2 Genetic Code of Mitochondria . . . . . . . . . . . . . 11.10.3 Strategies for Coding . . . . . . . . . . . . . . . . . . . . 11.11 Mechanics of DNA and RNA . . . . . . . . . . . . . . . . . . . . . 11.11.1 The Stretch of the Helix and the Stretch Modulus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.11.2 The Calculation of the Elastic Parameters of DsRNA/DsDNA . . . . . . . . . . . . . . . . . . . . . 11.11.3 Linkage of Twist and Stretch . . . . . . . . . . . . . . 11.11.4 What Is the Impact of MD Force Fields?. . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Transcription for Protein Biosynthesis . . . . . . . . . . . . . . . 12.1 Introduction to Protein Biosynthesis . . . . . . . . . . . . 12.2 Introduction to Transcription . . . . . . . . . . . . . . . . . . 12.3 Identification of Protein Binding Toward Genomic DNA Using ChIP-Chip and ChIP-Seq . . . . . . . . . . 12.4 Effect of Repressive Chromatin Structures on Transcription Activity . . . . . . . . . . . . . . . . . . . . . . . 12.5 Commonly Genes Possess a Canonical “Open” Nucleosome Organization . . . . . . . . . . . . . . . . . . . . 12.6 Sequence-Specific Factors Regulate Transcription of Specific Gene Sets . . . . . . . . . . . . . . . . . . . . . . . 12.6.1 Cis-Regulatory Elements . . . . . . . . . . . . . 12.6.2 Gene Regulatory Networks . . . . . . . . . . . 12.6.3 Sequence-Specific Regulators as Orchestrators . . . . . . . . . . . . . . . . . . . . 12.7 ATP-Driven Remodeling of the DNA on Nucleosomes . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.7.1 SWI/SNF Family and Its Relationship to Histone Acetylation . . . . . . . . . . . . . . .

447 448 448 448 449 450 450 451 451 453 455 456 458 460 461 462 464 466

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12.7.2 12.7.3

INO80/SWR1 Family. . . . . . . . . . . . . . . . . . . . Negative Regulation of Transcription by the ISWI Family . . . . . . . . . . . . . . . . . . . . . 12.7.4 CHD Family . . . . . . . . . . . . . . . . . . . . . . . . . . 12.8 Assembly of Transcription Factors for the Transcription Pre-initiation Complex . . . . . . . . . . . . . . . . 12.8.1 Role of Mediators . . . . . . . . . . . . . . . . . . . . . . 12.8.2 TBP is a Nucleator for the Assembly of PIC Within the NFR . . . . . . . . . . . . . . . . . . . . . . . . 12.8.3 Regulation of the Initiation of the Transcription by TFIIE and TFIIH . . . . . . . . . . 12.9 Effect of Pausing of the Pol II Directly After Start of the Transcription . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.10 Pol II CTD Serine 5 Phosphorylation During Initiation Triggers Regulation of Nascent RNA and Underlying Chromatin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.11 Pol II CTD Serine-5 Phosphorylation and H3K4 Methylation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.12 CTD Serine-2 Phosphorylation and H3K36 Methylation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.13 How is the Transcription Terminated? . . . . . . . . . . . . . . 12.14 Regulation of the Transcription of Gens by Noncoding Transcription . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.15 Is a Unique Transcription Cycle Employed for All Genes? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.16 Can Mechanical Stimulation of the Cells Alter the Transcription? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Splicing and Alternative Splicing and the Impact of Mechanics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.1 The General Principle of Splicing . . . . . . . . . . . . . . . . . . 13.2 The General Principle of Alternative Splicing . . . . . . . . . 13.2.1 Expression Profiling . . . . . . . . . . . . . . . . . . . . . 13.2.2 What Are Splice Junction Microarrays? . . . . . . 13.2.3 Exon Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.2.4 Tiled Genomic Arrays . . . . . . . . . . . . . . . . . . . 13.2.5 Other Proofing Techniques. . . . . . . . . . . . . . . . 13.2.6 Experimental Approaches Are Combined with Computational Approaches . . . . . . . . . . . 13.3 Discrepancy Between the Complexity of the Genome and the Proteome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.4 Why Do We Have Alternative Splicing?. . . . . . . . . . . . . 13.4.1 Are Most Alternative Isoforms not Functionally Important? . . . . . . . . . . . . . . . . . . 13.4.2 Underlying Molecular Mechanisms of Alternative Splicing . . . . . . . . . . . . . . . . . . .

486 487 487 488 488 488 489 490

490 491 492 494 495 496 496 499 509 509 514 515 515 516 517 517 518 520 522 524 524

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13.4.3

13.5

13.6 13.7 13.8 13.9 13.10 13.11 13.12

13.13

13.14

Cooperation of Alternative Splicing and Transcription . . . . . . . . . . . . . . . . . . . . . . . 13.4.4 Alternative Splicing Can Lead to Non-sense Mediated Decay . . . . . . . . . . . . . . . . . . . . . . . . 13.4.5 What Effect Has Trans-Splicing? . . . . . . . . . . . Can Alternative Splicing Be Regulated by Non-coding RNA? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.5.1 Long Non-coding RNAs . . . . . . . . . . . . . . . . . 13.5.2 Long Noncoding RNAs Remodel Chromatin . . . . . . . . . . . . . . . . . . . . . . . . . . . . Several Diseases Are Based on Alternative Splicing of Coding Genes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alternative Splicing in Cancer Disease . . . . . . . . . . . . . . Cancer Disease and Alternative Splicing of Fibronectin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Long Non-coding RNAs Act as Oncogenes or Tumor Suppressors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Other Diseases Alter the Alternative Splicing Variants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alternative Splicing Serves as an Escape Mechanism Cancer Cells During Drug Treatment . . . . . . . . . . . . . . . Regulation of Alternative Splicing by Mechanical Stimulation, Such as Matrix Mechanics . . . . . . . . . . . . . 13.12.1 Growth Factors Are Regulated by Mechanical Stress . . . . . . . . . . . . . . . . . . . . . . 13.12.2 Mechanical Stress Triggered Splice Variants of Genes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.12.3 Extracellular Matrix Proteins Are Regulated by Mechanical Stress . . . . . . . . . . . . . . . . . . . . 13.12.4 Analysis of the Mechanism of Alternative Splicing Due to the Mechanical Stimulation. . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.12.5 Various Functions of Mechanics Facilitated Splicing Variants . . . . . . . . . . . . . . . . . . . . . . . Mechanics-Based Alternative Splicing Regulates Extracellular Matrix Production, Such as Collagen . . . . . 13.13.1 The Alternatively Spliced Isoforms of COL2A1 Are IIA and IIB . . . . . . . . . . . . . . 13.13.2 The Other COL2A1 Isoforms Are IIC and IID . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.13.3 The Exon 2-coded Region of COL2A1 . . . . . . 13.13.4 The Cis Elements Regulate the COL2A1 Exon 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mouse Models for Col2a1 Alternative Splicing Investigations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.14.1 Transgenic Mice Exclusively Expressing Embryonic Col2a1 IIA Procollagen Isoform . . .

526 527 527 528 528 531 532 533 535 538 539 540 541 541 543 543

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13.14.2 The Splicing of Col2a1 and Type XI Collagen. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.14.3 The Mutations in the Alternatively Spliced COL2A1 Exon 2 . . . . . . . . . . . . . . . . . . . . . . . 13.14.4 Is the IIB Procollagen Required for the Healthy Cartilage? . . . . . . . . . . . . . . . . . . . . . . 13.14.5 Why is COL2A1 Exon 2 Targeted for Alternative Splicing, but not Homologous Domains in COL1A1 or COL3A1? . . . . . . . . . 13.14.6 Why is the Col2a1 IIA Isoform Reexpressed in Healthy Articular Cartilage in Mice? . . . . . . 13.15 Is There a Tissue-Specific Regulation of Alternative Splicing? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.16 Impact of Alternative Splicing on Cell Mechanics . . . . . 13.16.1 Alternative Splicing of Genes Encoding for Contractile Proteins Affects Cell Mechanics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.16.2 Impact of Alternative Splicing in Muscle Cells Mitochondria on the Functional Phenotype . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.16.3 Alternative Splicing of Genes Regulates Linkage of Excitation–contraction . . . . . . . . . . 13.16.4 Alternative Splicing Causes Diseases Involving Muscle Disturbance . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Translation and Post-translational Modifications in Protein Biosynthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.1 Introduction into the Process of Translation . . . . . . . . . . 14.2 The Machinery of Translation . . . . . . . . . . . . . . . . . . . . . 14.3 The Mechanism of Translation . . . . . . . . . . . . . . . . . . . . 14.4 Applicability of Single-Molecule Methods for the Process of Translation . . . . . . . . . . . . . . . . . . . . . 14.4.1 Laser Tweezers and Force Measurements . . . . 14.4.2 Thermodynamic Measurements and Kinetics . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.5 The Three Phases of Translation . . . . . . . . . . . . . . . . . . . 14.5.1 The Translation of Double-Stranded Hairpin MRNAs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.5.2 The Ribosome Acts as a Helicase . . . . . . . . . . 14.5.3 The Ribosome Functions as a Molecular Motor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.5.4 Manipulation of Nascent Protein Elongation and Folding . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.6 Post-translational Modifications . . . . . . . . . . . . . . . . . . . . 14.7 Influence of PTMs on Healthy and Diseased Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

560 561 562

562 563 563 565

565

566 567 568 571 595 595 596 597 599 601 602 607 607 609 611 611 614 615

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14.8

Effect of 14.8.1 14.8.2 14.8.3 14.8.4 14.8.5 14.8.6 14.8.7 14.8.8

Aging on PTMs . . . . . . . . . . . . . . . . . . . . . . . . Phosphorylation of Proteins . . . . . . . . . . . . . . . N-Acetylation of Proteins . . . . . . . . . . . . . . . . . Glycosylation of Proteins . . . . . . . . . . . . . . . . . Ubiquitination and Sumoylation of Proteins . . . S-Nitrosylation of Proteins . . . . . . . . . . . . . . . . Methylation of Proteins . . . . . . . . . . . . . . . . . . Oxidation of Proteins . . . . . . . . . . . . . . . . . . . . PTMs Act in Aging and Aging-Linked Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.8.9 Oxidation and Aggregation of Proteins . . . . . . 14.8.10 Chlorination of Proteins During in Aging . . . . 14.8.11 Nitration of Proteins. . . . . . . . . . . . . . . . . . . . . 14.9 Bacteria May Serve as a Model for PTMs in Aging and Aging-Linked Pathologies . . . . . . . . . . . . . . . . . . . . 14.10 Defects in the Protein Biosynthesis . . . . . . . . . . . . . . . . . 14.11 Impact of Translation and Post-translational Modifications on Cell Mechanics . . . . . . . . . . . . . . . . . . 14.11.1 HDACis Elevates H3K9ac on Mitotic Chromosomes but Do not Their Stiffness. . . . . 14.11.2 Methylstat Raises Mitotic Chromosome Stiffening and Histone Methylation . . . . . . . . . 14.11.3 Methylstat Causes no Alteration in SMC2 Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.11.4 Histone Hypermethylation Increases Stiffness of Mitotic Chromosomes, but Hyperacetylation Has no Effect . . . . . . . . . 14.11.5 The Model of Mitotic Chromosomes Encompasses Chromatin Interactions . . . . . . . . 14.12 Quality Control of Ribosome-Associated MRNA and Proteins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.12.1 Translational Nascent Chain Interactions Are Crucial for Protein Targeting and Folding . . . . 14.12.2 Ribosome-Linked MRNA Quality Maintenance . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.12.3 Nonsense-Mediated Decay . . . . . . . . . . . . . . . . 14.12.4 No-Go Decay . . . . . . . . . . . . . . . . . . . . . . . . . . 14.12.5 Non-stop Decay . . . . . . . . . . . . . . . . . . . . . . . . 14.12.6 Control of Aberrant Protein Production . . . . . . 14.12.7 Ribosome-Linked Quality Assessment of the Nascent Chain . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

620 620 621 621 622 622 623 624 624 626 628 629 630 632 632 634 635 636

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15 Cell Cycle, DNA Replication, Centrosomes, Centrioles and Cell Division . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.1 Introduction to the Steps of the Cell-Cycle and Cell Division . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2 Clock Theory or Domino Model . . . . . . . . . . . . . . . . . . . 15.3 Regulation of the Cell Cycle by Checkpoints . . . . . . . . . 15.3.1 Cell Cycle Checkpoints . . . . . . . . . . . . . . . . . . 15.3.2 Control of the Cell Size . . . . . . . . . . . . . . . . . . 15.3.3 Response to DNA Damage . . . . . . . . . . . . . . . 15.3.4 Surveillance of DNA Replication . . . . . . . . . . . 15.3.5 The Interrelation of S- and M-Phases. . . . . . . . 15.3.6 The Checkpoint of the Mitotic Spindle . . . . . . 15.4 Why Is the DNA Organized as a Twisted Helical Structure? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.5 DNA Replication Follows a Semi-conservative Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.5.1 How Can Cells Prepare for DNA Replication? . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.5.2 The Elongation Step of the DNA Replication . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.5.3 Enzymes Employed for DNA Replication . . . . 15.6 Active or Passive Role of Centrioles During Mitosis . . . 15.6.1 Function of Centrioles and Centrosomes During the Cell Cycle . . . . . . . . . . . . . . . . . . . 15.6.2 The Disengagement of the Centrioles . . . . . . . 15.6.3 Centrosome Linker . . . . . . . . . . . . . . . . . . . . . . 15.6.4 The Positioning of the Centrosome . . . . . . . . . 15.6.5 Centrosome Disjunction by Breaking up the Linker and Adjusting Their Nek2 Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.6.6 Actomyosin-Driven Movement of Centrosomes . . . . . . . . . . . . . . . . . . . . . . . . 15.6.7 How Are the Processes Timed? . . . . . . . . . . . . 15.7 Centrioles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.7.1 Historical View on Centrioles . . . . . . . . . . . . . 15.7.2 The Structure and Distribution of Centrioles . . . . . . . . . . . . . . . . . . . . . . . . . . 15.7.3 The Role of Centrioles in Cell Division Is Controversial . . . . . . . . . . . . . . . . . . . . . . . . 15.7.4 Fluctuations of Centrioles During Cell Division . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.7.5 Mitotic Spindles Are Formed Without Centrioles in Animals . . . . . . . . . . . . . . . . . . . 15.7.6 Inactivation of the Centrosome in Metazoan Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.7.7 Centriole-Free Animal Cell Lines . . . . . . . . . . 15.7.8 Acentriolar Mutants of Drosophila . . . . . . . . .

667 667 670 674 674 675 676 677 678 678 681 681 682 682 683 683 684 685 686 688

689 690 692 694 695 695 697 698 699 699 700 701

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15.7.9

Besides Centrosomes, There Are Complementary Pathways in Spindle Formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.8 Segregation of Chromosomes and Cytokinesis . . . . . . . . 15.9 Mechanisms in Developmental Processes . . . . . . . . . . . . 15.9.1 How Are Centrioles Inherited and Passed on? . . . . . . . . . . . . . . . . . . . . . . . . 15.9.2 Centrosomes and Centriole Cycle . . . . . . . . . . 15.9.3 The Generation of Daughter Centrioles . . . . . . 15.9.4 Guided Control of the Amount of Centrioles . . . . . . . . . . . . . . . . . . . . . . . . . . 15.9.5 Acts the Mitotic Spindle a Driver for the Segregation of Centrioles? . . . . . . . . . . . . . . . . 15.9.6 Inheritance of Centrosomes in Germline Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.9.7 Are Centrioles Active or Passive Players of the Cell Cycle? . . . . . . . . . . . . . . . . . . . . . . 15.9.8 Can the Mitotic Spindle Function as a Distributor of Centrioles? . . . . . . . . . . . . . 15.10 Centrosome Defects Can Lead to Cancer . . . . . . . . . . . . 15.10.1 Do Extra Centrosomes Promote Tumorigenesis? . . . . . . . . . . . . . . . . . . . . . . . . 15.10.2 Origin of Centrosome Defects in Cancer Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.10.3 What Are the Consequences of Centrosome Defects? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.10.4 Can Centrosomes Serve as Therapeutic Targets? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.11 Connection Between Cell Division and Cell Mechanics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.11.1 Two Distinct Spindle Dynamic Responses of the Developing Airway Epithelium . . . . . . . 15.11.2 The Dynamics of the Two Spindles Are in Line with Dividing Angles Distribution . . . . 15.11.3 Spatial and Temporal Relation Between Fixed and Rotating Spindles . . . . . . . . . . . . . . 15.11.4 Requirement of a Robust Ratio of the Two Spindle Types in Airway Shapes . . . . . . . . . . . 15.11.5 Fixed Spindles Employ the Cell Long Axis Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.11.6 A Cellular Mechanical Model May Explain Stable Ratio of Fixed and Rotating Spindles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.11.7 Stretching of Lung Explants Impacts Mitotic Spindle Angles Distribution in Developing Airway Epithelium . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

701 702 703 703 704 704 705 706 707 712 712 713 713 714 715 716 717 718 719 720 720 721

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16 Cell Proliferation, Survival, Necrosis and Apoptosis . . . . . . . . 16.1 Cell Proliferation and Survival . . . . . . . . . . . . . . . . . . . . 16.2 Apoptosis, Autophagy and Necrosis . . . . . . . . . . . . . . . . 16.2.1 Characteristics of Apoptosis . . . . . . . . . . . . . . . 16.2.2 Characteristics of Autophagy . . . . . . . . . . . . . . 16.2.3 Characteristics of Necrosis and Necroptosis . . . 16.3 Are the Three Different Types of Cell Death Interrelated? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.3.1 Linkage Between Apoptosis and Necroptosis . . . . . . . . . . . . . . . . . . . . . . . . 16.3.2 Linkage Between Autophagy and Apoptosis . . . . . . . . . . . . . . . . . . . . . . . . . 16.3.3 Linkage Between Autophagy and Necroptosis . . . . . . . . . . . . . . . . . . . . . . . . 16.3.4 Linkage Between Apoptosis, Autophagy and Necroptosis . . . . . . . . . . . . . . . . . . . . . . . . 16.4 Secondary Necrosis Is a Natural Result of the Apoptotic Program . . . . . . . . . . . . . . . . . . . . . . . . 16.4.1 Secondary Necrosis in Multicellular Animals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.4.2 Secondary Necrosis in Unicellular Eukaryotes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.4.3 Efferocytosis in Multicellular Animals . . . . . . . 16.4.4 Secondary Necrosis of Multicellular Animals Impacts Medicine . . . . . . . . . . . . . . . . . . . . . . . 16.4.5 How Can Cells Under Secondary Necrosis Be Identified?. . . . . . . . . . . . . . . . . . . . . . . . . . 16.5 Death-Induced Cell Proliferation Based on a Tissue Regeneration Mechanism . . . . . . . . . . . . . . . . . . . . . . . . 16.5.1 Death-Dependent Proliferation . . . . . . . . . . . . . 16.5.2 Mechanisms of Caspase-Driven AiP . . . . . . . . 16.5.3 Redundant or Context-Specific Mechanisms of AiP? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.5.4 Regulation of Caspases in AiP . . . . . . . . . . . . . 16.5.5 Regulating the Response: Feedback Loops Control AiP Activity . . . . . . . . . . . . . . . . . . . . 16.6 Role of Cell Death in Diseases . . . . . . . . . . . . . . . . . . . . 16.6.1 Apoptotic Cells Activate “Phoenix Rising” Route in Wound Healing and Tissue Regeneration . . . . . . . . . . . . . . . . . . . . . . . . . . 16.6.2 The Emerging Clinical Significance of Caspase-Driven AiP in Cancer . . . . . . . . . . 16.7 Tissue Regeneration Mechanism Hijacked by Tumors During Radiotherapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.8 Deregulated Apoptosis in Cancer . . . . . . . . . . . . . . . . . . 16.8.1 Cell Death: The Current Paradigm . . . . . . . . . .

743 743 745 746 748 750 752 752 754 754 755 756 757 758 760 760 762 763 764 765 767 768 770 770

773 774 778 778 780

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16.8.2

Compensatory Proliferation, Cell DeathBased Tissue Regeneration in Lower Organisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.8.3 Cell Death-induced Skin Wound Healing and Liver Regeneration . . . . . . . . . . . . . . . . . . 16.8.4 Cell Death Mechanisms and Radiotherapy . . . . 16.8.5 Tumor Repopulation upon Radiation Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.8.6 Cell Death-Driven Tumor Repopulation During Radiotherapy . . . . . . . . . . . . . . . . . . . . 16.9 Interplay Between Proliferation, Survival, Necrosis and Apoptosis and Cell Mechanics . . . . . . . . . . . . . . . . . 16.9.1 Cells Triggered by Mechanical Stress: Initial Hypothesis and Theoretical Grounds . . . . . . . . 16.9.2 Tissue Compaction Induces Cell Elimination . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.9.3 Experimental Proof of Mechanical Cell Competition . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.10 Mechanical Cell Competition Is Induced Through Cell Migration and Boundary Conditions . . . . . . . . . . . . . . . . 16.11 Molecular Pathways Sense Compaction and Drive Cell Elimination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.12 Mechanical-Based and Biochemical-Based Competition: Synergies and Antagonisms . . . . . . . . . . . . 16.13 Solid Stress in Tumor and Effect of Mechanical Cell Competition on Tumorigenesis . . . . . . . . . . . . . . . . . . . . 16.13.1 Features of Solid Stress in Tumors . . . . . . . . . 16.13.2 Space Competition with Stroma and Adjacent Healthy Cells . . . . . . . . . . . . . . . . . . 16.13.3 Intratumoral Space Competition . . . . . . . . . . . . 16.13.4 Therapeutic Routes . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Metabolic Pathways of Eukaryotes and Connection to Cell Mechanics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.1 The Metabolism Impacts on Every Cellular Task . . . . . . 17.1.1 Genetic Alterations in Human Metabolism . . . 17.1.2 Cancer Fosters Genetically-Based Metabolic Alterations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.1.3 Perspectives, Boundaries and Applications. . . . 17.2 Citrate Acid Cycle [The Tricarboxylic Acid (TCA) and Urea Cycles (Krebs)] . . . . . . . . . . . . . . . . . . . . . . . . 17.3 Glycogen Catabolism or Cori Cycle . . . . . . . . . . . . . . . . 17.4 Mechanical Properties of the Tumor Drive Metabolic Dysfunctions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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17.5

Introduction into Desmoplasia-Driven Glycolysis . . . . . . 17.5.1 The Tumor Microenvironment, Such as Matrix Dynamics, Matrix Cross-Linking and Mechanical Signaling, Alters Cancer Cells . . . 17.5.2 Mechanosignaling and Cellular Tension Fosters Cancer Progression . . . . . . . . . . . . . . . 17.5.3 Experimental Techniques Determine Extracellular Matrix Stiffness and Tissue Mechanics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.6 Glycolysis and Metabolism During Oncogenesis . . . . . . 17.6.1 Tumor Redox . . . . . . . . . . . . . . . . . . . . . . . . . . 17.6.2 Therapeutic Targeting of Tumor Metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.6.3 Techniques for Metabolic Imaging . . . . . . . . . . 17.7 Tissue Mechanics-Based Regulation of the Metabolism of Cancer Cell and Cancer Progression . . . . . . . . . . . . . . 17.7.1 Physical Forces Impact the Metabolism of Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.7.2 Tissue Stiffness Controls the Metabolism and Progression of Cancer . . . . . . . . . . . . . . . . 17.8 Tumor-Stroma Mechanics Regulate the Amino Acid Concentration for Tumor Growth . . . . . . . . . . . . . . . . . . 17.8.1 Mechanical Cues Lead to Metabolic Reprogramming and Coordinated Non-essential Amino Acid Exchange in the Tumor Niche . . . . . . . . . . . . . . . . . . . . . 17.8.2 In a Stiff Surrounding, Elevated GLS1 Expression and Glutamine Metabolism Contribute to Metabolic Reprogramming . . . . . 17.8.3 Aspartate and Glutathione Impact the Cancer Cells or CAFs . . . . . . . . . . . . . . . . . . . . . . . . . 17.8.4 SLC1A3 Provides Aspartate/Glutamate Exchange in the Tumor Niche to Foster Cancer Progression . . . . . . . . . . . . . . . . . . . . . 17.9 Alteration of YAP/TAZ-Driven Mechanotransduction Pathway Guides Metabolic Reprogramming in Cancer Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.9.1 The Mechanotransduction Cascade Governs Metabolic Reprogramming . . . . . . . . . . . . . . . . 17.9.2 Exchanges of Aspartate/glutamate Inside the Tumor . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.9.3 The Expression of GLS1 and SLC1A3 Is Increased in HNSCC Tumors . . . . . . . . . . . . 17.10 Glutathion Synthesis and Mesenchymal Cell State . . . . .

843

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848 849 851 851 852 853 853 853 856

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859 860

861

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17.11 Connection Between Metabolism and Cell Mechanics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.11.1 Metabolic Rewiring During the Malignant Progression of Cancer . . . . . . . . . . . . . . . . . . . 17.11.2 Connection Between Cancer Stemness, EMT and the Plasticity in Metabolism . . . . . . . . . . . 17.11.3 The Metabolism of Cancer Cells Depends on Biomechanics, Tissue Stiffness and Energetics . . . . . . . . . . . . . . . . . . . . . . . . . 17.11.4 Changes in Cell–Matrix Adhesion Strength Impacts Metabolic Pathways . . . . . . . . . . . . . . 17.11.5 Alterations in Intercellular Adhesions Impacts the Cancer Metabolism . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

867 867 869

869 871 871 876

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 893

Part I Introduction into Biophysics and Basic Biological Processes from a Physical Point-of-view

Part 1 of the book Cellular Mechanics and Biophysics introduces the reader to the biophysics research field and provides insights into fundamental processes of biological research from a biophysical point of view. After the introduction of the field, basic knowledge of classical cell and molecular biology is conveyed, which is necessary for the understanding of the following chapters. This first part discusses the main differences between prokaryotes and eukaryotes and presents a theory for the development of eukaryotes. The most important cell compartments (termed organelles), such as nucleus, mitochondria, endoplasmic reticulum, Golgi apparatus,vacuoles, lysosomes and peroxisomes, are presented, and their individual functions are briefly outlined. More precisely, a special focus lies on the impact of cell organelles on the overall cell mechanical properties. All chapters of the first part will help the reader, even if they have limited knowledge of

biology, to understand the field of biophysics and subsequently to better convey an understanding of the complexity of biological matter. The first part serves also as a basic knowledge reservoir for the other chapters in part 2 and introduces the reader into the complex world of living biological matter. In any case, this first part explains the most important classical biological components, cellular functions and processes from a biophysical point of view and forms the basis for understanding the following two other volumes of the Cellular Mechanics and Biophysics book. The purely biological view, which is available in many review articles and in the introductory part of research articles, is presented here, if possible, together with the biomechanical aspects. Finally, state-of-the-art biophysical research is included and integrated into the classical field of cellular and molecular biology.

1

The Definition of Biophysics: What Exactly is Biophysics?

Abstract

This chapter describes what kind of research is performed in biophysics and what major breakthroughs biophysics has brought. The big problem for the field of biology and life sciences is that we have too much information about all molecules and their signaling pathways. We are simply lost in all the information and cannot decipher the essential parts. Thus, we need to focus on the framework helping us to organize and categorize all those endless numbers of facts. Biologists and life scientists wonder why a physicist is often seen as a reductionist who wants to leave out all the details that distinguish cells from metals. The reason for the reduction is that the whole unified framework must finally be seen in the context of an overall picture. The reduction to the main characteristics stands in contrast to the diversity and abundance of the mechanisms and interaction pathways that provide a fruitful, but also enormous tension. Thus, future research will at the first glance combine these seemingly contradictory directions and, if necessary, switch back and forth between them in order to understand these living beings. There has been a tremendous development of several physical techniques suitable for the measuring of living biological matter, and there is an approach revealing what happens in the nano- and microenvironment of cells. Moreover, a lot of physical ideas and principles are behind the scientific cartoons and schematic drawings present in cell biology and molecular biology books, which may serve as suitable tests to confirm or reject these hypotheses. In general, the biological question of how living organisms can be highly ordered differs from the physical question of whether the flow of energy can evoke increased order. Basically, both scientists, the biologist and physicist, achieve the same thing, but have very different approaches to getting answers and reaching their goals. In detail, this chapter gives a brief overview of the field of biophysics and also mentions subareas. The main objective of this chapter is to develop an understanding of biological processes and mechanisms and to sensitize the reader to the great diversity observed in the study of living materials in biological processes that can be attained in all fields of biology.

1.1

Introduction to Biophysics

Biophysics (also known as biological physics) is an interdisciplinary science, in which principles of physics and chemistry are employed to understand living matter. In addition, methods of mathematical analysis and computer modeling are used to demonstrate how the mechanisms of biological © Springer Nature Switzerland AG 2020 C. T. Mierke, Cellular Mechanics and Biophysics, Biological and Medical Physics, Biomedical Engineering, https://doi.org/10.1007/978-3-030-58532-7_1

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systems work. As the name biophysics implies, biology and physics are involved. In biophysics, both disciplines are included at equal proportions (Fig. 1.1). In general, the studies in biology are about life at all levels and are broadly ranged with a great diversity and maximum complexity. Biology simply examines how living organisms eat, communicate with others, live and interact with their environment and divide or reproduce. Physics usually describes nature through mathematical laws to predict the forces running idealized systems. Biophysics attempts to combine the complexity of life with relatively simple physical laws in order to promote the understanding of basic regulatory mechanisms and to gain insights into biological research topics. In specific, biophysics follows general principles to define patterns in life. These principles are described by physical laws to predict unknown patterns of different biological topics precisely. These hypotheses can be tested by applying physical methods with biophysical devices. Finally, biophysics is the investigation of biological systems and processes based on physical principles, which will be proven and manifested or withdrawn. Biophysics is a molecular science, which aims to explain biological functions through the investigation of the molecular structures and the characterization of the properties of specific molecules. The length scales of these molecular structures vary widely, since the size of these molecules is largely diverse (Fig. 1.2). For example, small fatty acids and sugars are characterized by a contour length of 1 nm, which is the length of a molecule or aggregate at maximum physically possible extension. Moreover, macromolecules such as proteins have a contour length of 5–10 nm, starches possess a contour length of more than 1000 nm, and elongated DNA molecules have a contour length of more than 1 cm and a width of only 20 nm. These biomolecules represent the simple and sole building blocks of all living organisms, since they assemble into cells, aggregates of cells, such as cell clusters and tissues and finally whole organisms. The complex structures formed by biomolecules can be visualized under a conventional light microscope. What do biophysicists study? Biophysicists study the complexity of life at different levels such as various length scales, which ranges from single atoms or molecules to “real” living cells of

Fig. 1.1 What do we understand by biophysics? Biophysics combines the complexity of life with the simplicity of physical laws in a balanced manner. The study of biophysics leads to principles and models after observation of pattern formation in life processes. Based on these models, accurate predictions are feasible

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Fig. 1.2 Length scales of biophysical measurements vary largely

prokaryotic or eukaryotic origin, whole tissues or organisms. In physical terms, the length scales range from nanoscopic (molecular processes), mesoscopic (intracellular organization) to microscopic levels (multi-cellular tissues). Innovations, especially those in devices, come from biophysical laboratories, and thus, even new fields of research are opened up. Finally, the scientific work aims to understand how biological systems function. Therefore, biophysicists may ask the following simple questions: How do cellular machines work to produce proteins? The molecular machines need to be relatively small, but they seem to function based on the same principles of machines used in other disciplines such as several branches of industrial productions. These molecular machines consume energy to perform work. One example is the kinesin machine that can be seen a machine carrying a load that is transported by using a cellular track such a microtubule filament. This process has been described by biophysical approaches, focusing on how each step is driven forward to transport a cargo. How do cells communicate among each other? Biophysicists created colored tags being attached to proteins to reveal the location of a certain protein within the cell and determine the protein’s turnover dynamics. These colored tags exist in different colors and can be used for co-staining and multiple staining in cells, when the cells are still viable. Thus, an individual protein can be tracked during its function in many pathways. The overall question is how essential is biophysics for the further development of biological research? Biophysics revealed how atoms are arranged to function in building DNA and proteins. Protein molecules undergo chemical reactions to provide, for example, cellular functions such as the motility of single or collective movement of entire cells through the extracellular matrix of connective tissue. Moreover, proteins provide functions of cells and tissues such as the recognition of transmigrating immune cells through the endothelial lining of blood or lymph vessels during acute inflammation upon tissue injury. In this special case, the proteins help to sense the environment and to facilitate the interaction between immune cells and endothelial cells. Another major function of proteins is to digest food in order to get energy. In addition, proteins regulate immunological processes such as host defense, cause illness, repair DNA and regulate cellular growth, transmit electrical signals in the brain, read DNA and replicate DNA and pass it on to

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future generations. Since there are large variations in proteins, their reaction to pharmacological drugs can be very different. The understanding of these differences is useful for the precise diagnosis and in determining differences in drug response. This knowledge can then help to develop and improve drug design, drug vehicles for their efficient and proper delivery, controlled release and storage, which are useful in the treatment of diseases that lead to individualized drug treatments due to the special needs of patients. What has been achieved by biophysics in the past is still a major breakthrough? In the 1940s, it has been shown that genes are built of simple four DNA bases such as adenine (A), cytosine (C), guanine (G) and thymine (T). How can this simple chemical structure include the information of DNA to be inherited to future generations? What is the mystery of this structure? In 1953, the structure of the DNA as a double helix has been discovered. The discovery of the DNA structure sheds light on how simple it is to get just by small variations in the structure unique individuals and even well-defined species. These major discoveries have provided the path for understanding the molecular machinery of life. During the 2000s, the human genome was fully sequenced and hence discovered, which was only possible through the further improvement of a biophysical device, namely the technique of sequencing the genome by using a laser-based approach that employs individual, differently colored tags attached to each type of the four DNA bases. Since the entire genome from more than 200 species was sequenced, this knowledge forms the basis for understanding how some genes were conserved in different species. Hence, the relationships between different genes can be analyzed easily based on the knowledge of their homology in the sequence using computational methods. Beyond that, the knowledge gained is regarded as useful for the prevention and cure of diseases and the understanding of cell functions. Besides these major breakthroughs, biophysics also leads to the development of suitable physical models and pre-supposes the knowledge of biological parameters affecting the system of interest and the implementation of physical or chemical principles. Many books on cellular biology, molecular biology and genetics miss this part and simply describe processes in a very detailed way without pointing out the most important regulatory steps required or the underlying principles. In biophysics, a working model is first created, based on the data obtained from quantitative experiments, and the parameters involved in a particular process are determined. The second step is to decide which characteristics concern a problem and are therefore of central importance or which characteristics are less important and can hence be omitted. Beyond the development of physical models, the facts about an individual system of interest must to be disclosed. There are numerous facts about individual systems, some of them with a high level of certainty, while others are relatively uncertain. Moreover, there are statements of a first category that are easy to understand and confirm, such as the statement that prokaryotes have no nucleus and no organelles. In addition, there are statements of a second category, which can be confirmed by numerous experiments using a variety of experimental methods. An example of such a statement is that genes containing introns need to remove these introns from the coding sequence in a large and complex machinery, which is termed spliceosome. After all, only the exons form the coding sequence. A statement of the third category is a compelling explanation for an observation without a proof by a biological experiment, and it depends exclusively on assumptions and can hence be highly speculative. The endosymbiotic hypothesis falls into this category because it states that the mitochondria of eukaryotic cells are the descendants of a former free-living and independent bacterium which, after entering the eukaryotic host, has developed a symbiotic relationship with a eukaryotic ancestor cell. The hypothesis is based on observations, and its interpretations represent a modeling approach that definitely needs to be improved. These hypotheses must comply with the existing laws of physics and chemistry and hence lead to improved understanding of

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how biological systems will react. A physical principle is first and foremost that the average energy of a molecule increases with increasing temperature of its microenvironment, and a chemical principle is that oil and water cannot mix.

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Historical Overview of Selected Early Developments in Biophysics with a Focus on Cell Mechanics

The onset of the analysis of protoplasmic physical properties started over 100 years ago, which is the timepoint where we have documentary work available. Nowadays, intricate techniques, such as atomic force microscopy (AFM), cell poking, particle rheology, cell stretching and confocal laser scanning microscopy, have been developed and further improved to access and quantify the mechanical properties of cells, cell clusters, tissues, such as biopsies or surgical resections or extracellular matrix material. However, historically, the mechanical properties of cells have been solely described qualitatively. Although the techniques used in the past are not suitable for up-to-date the analysis of cell mechanics, the historic view starting with the early roots may help us to understand nanoscientific approaches and nanomedicine techniques employed today or in the future. Biophysics has often been considered a thriving discipline, as it continues to develop and expand as an interdisciplinary science through the combination of several disciplines. Due to its rapid growth and many directions of research that are implicated in the numerous different research topics, many researchers have a problem to describe what biophysics means to them or what the general meaning of biophysics is. Hence, biophysicists often fail to describe what biophysics represents. In the following, a historical view on biophysics is presented in order to provide an encompassing description of biophysical research topics. It all started more than 100 years ago, in the seventeenth century, when Robert Hooke and Antony van Leeuwenhoek explored very small living matter using simple optical microscopy, in which fluid and cellular movements seemed to be of the extreme type and depended on different length and time scales (Fig. 1.3). More precisely, the ciliate named Vorticella appeared to move in a bell-shape manner combined with a stirring of water molecules in the middle of the round opening. The motion of particles within this environment was also seen by others, such as the Brownian motion of particles and even organelles within living cells (Heilbrunn 1926, 1927). More precisely, it seems to be suitable to measure the motion quantitatively in order to determine the viscosity. However, techniques

Fig. 1.3 Time scales of biophysical analysis can be performed over a wide range

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to measure the microrheological properties or to perform nanoindentation analysis were not invented that time. Based on the material testing techniques, the living matter, such as cells and tissues, can be characterized in detail, and hence, theories can be formulated, such as indentation techniques, the bending of beams and the Hertz model in the nineteenth century (Thompson 1917). More precisely, the material properties for different kinds of wood or alloys have been revealed and have led to the concept of the adaption of tissue, such as the Wolff’s law that is applied to the skeleton (Thompson 1917). As the nineteenth century was almost over, the mechanical properties of living matter, such as cells, were investigated with a broad range of biophysical techniques that are related to engineering mechanics on a macroscopic length scale. Although much time has passed, nanoscale probing techniques and theoretical treatment of biological organisms are still based on nineteenth-century techniques and models (Wang and Discher 2007; Boal 2002; Mofrad and Kamm 2006). However, the living matter, such as a cell, represents a universe itself. This universe seems to be very complex and is subject to forces and dynamics that are somehow connected with its function. In former times, the role of mechanical forces in biology has been under debate (Thompson 1917), whereas now there is agreement about the important function of mechanical forces in cells and tissues. Hence, mechanical laws can be applied to living matter and mechanical properties of cells have been started to be determined. In the next step, the theoretical understanding revealed that a compelling mechanical picture of the cell is required, but under debate that time (Heilbrunn 1924, 1926, 1927; Bingham 1933). In detail, at that time cells were seen as homogeneous gels or sols, and as viscoelastic and plastic fluids. These descriptions are still commonly used to describe living cells, although multiple models have been presented to describe cell mechanics in a different manner, such as a connected discrete mechanical elements, a viscoelastic continuum and a combination of a viscoelastic fluid in a dense meshwork (Wang and Discher 2007; Boal 2002; Mofrad and Kamm 2006; Bao and Suresh 2003; Charras et al. 2005; Dai and Sheetz 1998; Heidemann et al. 2000; Huang et al. 2004; Ingber 2000; Kasza et al. 2007). For all these, different models seem to be an experimental proof or disproof that leads to support or reject of the model, such as the soft glass rheology phenomenon (Trepat et al. 2007; Stamenovic et al. 2007). Since the experiments have been refined in the last century, viscosity elasticity, plasticity and motion have been analyzed more precisely. Hence, there exists no comprehensive theoretical model of cell mechanics, which can account timedependent alterations and predict changes accurately. Moreover, there is no agreement as to whether the mechanical phenomena and properties are exclusively by-products of biological functions and processes or whether they are regulated at genetic and physiological level by feedback loops, actuation and/or response signal transduction pathways. In addition, the mechanical properties have been supposed to regulate cellular functions, such as cell adhesion and motility. Previously, some studies have tried to answer this question (Even-Ram et al. 2006; Engler et al. 2006; Paszek et al. 2005; Ingber 2005, 2006). A major focus is AFM studies of cell mechanics, although there are many other biophysical techniques probing cell mechanics (Wang and Discher 2007; Boal 2002; Mofrad and Kamm 2006; Mierke et al. 2008a, b; Mierke 2011). The cell mechanics research is currently still growing and connected to other disciplines, including cell biology, molecular biology, biochemistry, cell (nano-)physiology. This chapter focuses the historic view on the development of cell mechanics using the AFM technique.

1.2.1 Isolated Protoplasmic Mechanics It has been demonstrated that resected blood vessels and nerves can be probed mechanically in lectures (Stuart 1738). Thereby, concepts of hydrostatics, elasticity and viscoelastic fluids were mentioned, and it has turned out that nerves are inelastic. In living matter, these mechanical

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oscillations were largely investigated, such as the spontaneous heart contractions outside the organism (Paget 1857). These heart contractions can pertain without a proper nervous system. In line with these results, heart and muscle cells showed the same behavior using AFM (Domke et al. 1999; Shroff et al. 1995; Pelling et al. 2007a, b; Haupt et al. 2006). These oscillations have been found in many other organisms, such as in vacuoles, cell walls of plants or the cilia movement in microorganisms (Paget 1857). All these structures have in common that they possess no muscle structure or a nervous system. There was no explanation why these organisms perform mechanical oscillations. The concept of biological mechanics still not yet fully formulated. These early investigations of mechanical phenotypes were focused on viscosity of the protoplasm due to the unavailability of microscopic techniques. However, the cytoplasmic streaming (circular flow of eukaryotic cytoplasm) was detected early (Heilbrunn 1927; Ewart 1901) and had been employed to determine protoplasmic viscosity in a qualitative manner. The application of particle tracking in cell mechanics started with viscosity measurements (Heilbrunn 1926, 1927) and hence is regarded as the earliest uses of particle tracking in cell mechanics that led to the development of nanoscale particle tracking and microrheological measurements (Weihs et al. 2006; Mason et al. 1997). There has been discussion about the influence of the particle size, the mesh size of the protoplasm, cell damage and experimental conditions such as temperature on the mechanical properties (Heilbrunn 1926, 1927; Valentine et al. 2004). These points are still under investigation in modern particle tracking approaches and need to be addressed. In addition, there is also an effect that the particles supplied externally and taken up by the cells affect their function, such as their motility in 3D confinements (Mierke 2013). Another approach has been reported in 1924 using a newly developed early magnetic microscope (Seifritz 1924), which was used to oscillate nickel particles (of approximately 16 lm in diameter) in living cells. This approach is similar to modern particle microrheology (Trepat et al. 2007; Stamenovic et al. 2007; Weihs et al. 2006; Mason et al. 1997) and conceptually similar to magnetic beadtwisting cytometry (Valberg and Albertini 1985; Lele et al. 2007; Massiera et al. 2007). Another experimental approach of magnetic manipulation employed the injection of iron particles into living bacteria that are altered in their position by an external electromagnet (Heilbrunn 1927). Another approach to analyze the viscosity at the time was the centrifugation of the cells. Thereby, the cell granules are displaced to one end of the cell and their migration back to their original position is monitored that revealed an estimated protoplasm viscosity (Heilbrunn 1927). Alterations in the range of two orders of magnitude in the viscosity were observed during the mitosis and fertilization of sea urchin eggs (Heilbrunn 1927). In detail, when the viscosity is kept constant, the mitosis can be impaired (Heilbrunn 1920a, b). More precisely the protoplasmic viscosity can be altered by temperature, distinct chemicals, such as anesthetic agents, salt or organic solvents, radiation and electric currents (Heilbrunn 1926, 1927; Seifriz 1931; Heilbrunn 1920a; Heilbrunn 1925a, b; Heilbrunn and Wilson 1948, 1957, Heilbrunn et al. 1954; Seifriz and Uraguchi 1941; Scarth1924; Packard 1931; Lepeschkin 1932; Jacobs 1922; Forbes and Thacher 1925; Carlson 1946; Bayliss 1920; Angerer 1939; Addoms 1927). Within the last past two decades, the Young’s moduli (synonymously termed elasticity or stiffness) have been determined using AFM. The Young’s modulus is fundamentally different to the viscosity, but somehow related to it. Thereby, anti-cytoskeletal drugs (Rotsch and Radmacher 2000; Pelling et al. 2007a, b; Northern 1950), chemotherapy reagents (Heilbrunn and Wilson 1957; Lam et al. 2007) and electrical stimulation (Greely 1904; Zhang et al. 2001) and their impact on the Young’s modulus has been determined. More precisely, the majority of AFM measurements with living cells rely on the employment of the nano-indentation mode in order to reveal mechanical parameters from the resulting force-distance (or synonymously termed force–displacement) curves (Binning et al. 1986). The major mechanical parameter is the elasticity, but other rheological parameters were also determined in living cells (Alcaraz et al. 2003; Smith et al. 2005,

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2007). Indentation approaches have been utilized together with scanning of the cell to obtain full force maps (Rotsch and Radmacher 2000; Rotsch et al. 1997, 1999; Radmacher 2002, 2007). Alternatively, single spots on living cells have been analyzed to investigate time dependence (Pelling et al. 2007a, b; Lam et al. 2007). A century ago, the initial indentation approaches on living cells deal with glass microneedles that can be slowly inserted into multiple cell types to estimate their viscosity (Seifriz 1918; Kite 1913; Chambers 1915; Chambers and Fell 1931). The microdissection technique and its variants have often been used to study the protoplasm mechanics, although it is exclusively a qualitative analysis. In addition, a modified microneedle experiment has been performed by injecting the microneedle in living cells in order to push and into organelles (Chambers and Fell 1931). In 2005, a similar approach has been reported, in which a modified AFM cantilever tip (termed nanoneedle) was used to push and penetrate the largest organelle in the living cell, the nucleus (Obataya et al. 2005). These two techniques have been employed to measure similar mechanical properties, such as the penetration and deformation of the nucleus. An advantage of the AFM techniques over the “old” microneedle technique was that quantitative measurements of the force can be performed. Moreover, the simultaneous laser scanning confocal during the AFM measurement procedure delivered an accurate threedimensional information during the mechanical stress application. Before the AFM was developed in 1986, another technique termed cell poking was presented that deals with calibrated microneedles (McConnaughey and Petersen 1980; Petersen et al. 1982; Daily et al. 1984) (Fig. 1.4). Dissimilar to earlier techniques that pushed the needle through the entire cell, the cell poking needle solely indented the plasma membrane of the cell in order to determine the cell deformation and elasticity. More precisely, the contribution of cytoskeletal components was investigated using anti-cytoskeletal drugs (Petersen et al. 1982). Early examples for bulk cell mechanical properties were plant cells whose elasticity has been revealed (Treitel 1944). In detail, plant tissue was clamped with one end in a stretching device and the tissue was stretched using calibrated weights in order to obtain stress–strain curves of the tissue. This concept is similar to current and future directions of cell and tissue mechanics that analyze cell clusters (termed cell spheroids), cell monolayers and distinct tissues (Huang et al. 2004; Ingber 2005, 2006; Bray 1984). In addition, whole cell mechanics (Waugh and Evans 1979; Evans and Hochmuth 1976; Evans and Yeung 1989;

Fig. 1.4 Simple setup of the traditional cell poking technique

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Hochmuth 2000) and nuclear mechanics (Lammerding et al. 2004, 2006, 2007; Rowat et al. 2006) can be determined using micropipette aspiration, where the cell is sucked in a micropipette. In addition, microplates (Thoumine et al. 1999; Smith et al. 2000) have been used to analyze cell deformation and elasticity due to force exertion. The adhesion forces of cells playing an important role in cell spreading and migration have been determined by rupturing cells from a surface using a cantilever (Sagvolden et al. 1999). Since cell migration has been analyzed in order to investigate wound healing processes, it has turned out that this process involves cell mechanics (Herrick 1932). Using AFM , protrusive forces at the edge of motile cells can be determined (Radmacher 2007; Prass et al. 2006). In complement with these measurements, traction forces can be analyzed on these 2D substrates (Pelham and Wang 1999; Jurado et al. 2005) and additionally micropipette and laser trap measurements can be performed (Li et al. 2005). Since cell migration and invasion are key elements in the malignant progression of cancer, such as cancer metastasis, it has been hypothesized that the increased invasive potential of malignant cancer cells is related to altered cell mechanics. Thus, cell mechanics have been analyzed by optically trapping and stretching cells in electromagnetic fields to determine their mechanical properties that are corrected with their metastatic potential (Svoboda and Block 1994; Svoboda et al. 1992; Guck et al. 2001, 2005). These assays have been developed in addition to initial cell deformation assays (Ochalek et al. 1988). Moreover, magnetic traps were employed to examine the rheological properties of cells. Hence, super-paramagnetic beads were coupled to cells in order to probe them mechanically by altering the magnetic field (Stamenovic et al. 2007; Wang and Ingber 1994; Puig-De-Morales et al. 2001). Thereby, the mechanical properties of cells such as the deformation of organelles by external forces and the force transmission processes in cells were revealed using the magnetic (bead-)twisting cytometry (Wang and Ingber 1994; Hu et al. 2004). The concept has similarities to earlier investigations by the Seifriz group (Seifriz 1924). In detail, he reported with the magnetic microscope and the simple tracking of organelles due to force application by indentation of the cells with micropipettes (Puig-De-Morales et al. 2001). Over more than 100 years ago, a wide variety of techniques have been developed to analyze the mechanical properties of living cells, such as cancer cells. Initially, the mechanical characterization of living cells and organisms was highly conceptual; however, there was an enormous increase in biophysical techniques probing cellular mechanics (Wang and Discher 2007; Boal 2002; Mofrad and Kamm 2006). At that time, an increasing number of laboratories established successfully the measurement of cell mechanics. Many questions that have been asked for years are still the same that needs to be examined today. It is now clear that living soft matter, such as cells and tissues, exhibits distinct mechanical properties that seem to drive physiological processes, such as proliferation and apoptosis and pathological processes in multiple diseases, such as the malignant progression of cancer. A key future development of the field of nanomedicine or nanophysiology seems to be the identification of specific mechanical states and their reliable detection. Since the concepts have been reported for several years, the question can be raised, whether there can be performed another action than just developing and improving accurate biophysical tools to measure cell and tissue mechanics.

1.2.2 Future View on Cell Mechanics In 1737, Stuart has initiated the discussion whether the heart can be regulated by well-defined stimulation. The accurate measurement of the mechanical parameters is a key feature of biophysical research, and in addition, the controlled regulation of biological pathways seems to be another key feature that needs to be addressed by altering cell mechanics. Mechanical force that is applied by the

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cantilever tip using AFM has caused multiple chemomechanical responses (Charras and Horton 2002; Charras et al. 2001; Formigli et al. 2005). It has also been reported that the mechanical environment of many cell types, such as cancer cells and stem cells, can be employed to precisely regulate the control of gene expression and differentiation pathways (Even-Ram et al. 2006; Engler et al. 2006; Paszek et al. 2005; Ingber 2005, 2006; Wang and Ingber 1994; Huang and Ingber 2005; Suresh 2007). Moreover, it can also be used to alter gene expression and differentiation pathways (Engler et al. 2006). Since all the tools to analyze the mechanical properties have been available, we can probe cells with well-defined forces and even alter the mechanical properties of the microenvironment of the cells. In addition, we can also monitor the mechanical properties of the microenvironment. It seems to be only a small step from here to the initiation and control of biological pathways in defined cell cultures and additionally in the future in vivo settings. In the future, it seems likely that the emerging field of nanophysiology will become a new branch of the nanomechanical regulation of biological pathways. This ill-conceived area of research, combined with ultrasensitive detection technologies and modern pharmaceutical treatments, can play an important role in the advancement of nanomedicine and the diagnosis and treatment of diseases. Since the physical principles that regulate cell mechanics are yet clearly understood, the field of nanotechnology can be envisioned to be helpful in medicine, such as cancer research and physiology. However, the entire field is currently under strong debate. In detail, the elasticity concept seems to be rather ill-defined in a living cell. The cell is no static and homogeneous object; instead, the cells are rather heterogeneous and undergo continuously dynamical remodeling of its cytoskeleton and is rather likely highly anisotropic. Hence, the Hertz model, which is usually employed in nanoindentation experiments using AFM, cannot applied in an ideal manner. Moreover, cellular Poisson ratio appears to be rather undefined and is conventionally considered constant, even though this may not be the case. However, there is no evidence presented that the Poisson ratio itself is not affected even during physiological processes and that the variation in the Poisson ratio does not correlate with Young’s modulus alterations. Finally, the theoretical descriptions of cell mechanics will still need further improvement. However, there is clearly no doubt that future discussions will take place in this issue. The conclusions in cell mechanics are usually drawn from investigations of an individual cell type, which has been analyzed under limited conditions. Moreover, these conclusions were generalized to a wide range of different cell types or even to all cell types. However, it has also been suggested that cell mechanicals and the biochemical/structural requirement for mechanical properties of cells are cell type-dependent and additionally they even depend on the physiological and mechanical microenvironments of cells (Mierke 2019a, b). Many different cell types possess the same structural components, such as the cytoplasm, cytoskeleton, nucleus, membranes and other organelles; however, it is not convincingly shown that they all use the same signal transduction pathways during these biological processes. Hence, besides the identification of a unified theory of cell mechanics, it should also be taken into account that there might be different phenotypic mechanical responses including different signal transduction pathways due to the broad heterogeneity of the living cells. The classification of cells may be based on individual mechanical models or on a combination of different models best suited to the mechanical reactions and internal structures required for mechanotransduction or during physiological processes. The trend in nanoscientific research is becoming interdisciplinary and is one of the greatest advances in the field of cell mechanics. Historically, the research field of biophysics emerged as a distinct discipline and started originally as a group of four including Emil du Bois-Reymond, Ernst von Bruecke, Hermann von Helmholtz and Carl Ludwig, which are all four physicians. Emil du Bois-Reymond, Ernst von Bruecke and Hermann von Helmholtz are former students of the German physiologist Johannes Mueller. In 1847, they all developed a research project which had been rejected with the notion that living animals

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depend on specific biological laws, and hence, physical laws cannot be applied to them. Moreover, it had been stated that these vital forces of living matter would be different to those forces present in inorganic matter. Thus, the group had to explain why biological processes or phenomena exhibit the same laws that can be used to describe and elucidate physical and chemical phenomena. It has been mentioned by Ludwig and also said by Cranefield (1957) that physiology should be based on a chemico-physical foundation and it should be scientifically ranked equally as physics. Moreover, the term of “Organic Physics” has been created, which had been used by du Bois-Reymond. However, this term did not develop as expected, and hence, in 1982, Karl Pearson created the novel term “BioPhysics” in “The Grammar of Science” (Pearson 1900) that should describe science connecting physical and biological science. However, he also stated that this novel branch of science has not developed fully at present and improvement is highly needed as it may become important in the future. Indeed, it worked out well for the novel field as Julius Bernstein reported a possible mechanistic basis for the development of transmembrane potential differences, which relies on electrodiffusion work by Nernst and Planck (Bernstein 1902). Several years later, Archibald V. Hill described the Hill equation (Hill 1910). Both studies are prototypical examples of biophysics for quantitative analysis of biological phenomena. The main research branch in biophysics in the twentieth century was neurophysiology and muscle physiology that are well suited for quantitative analysis. In the second half of the twentieth century, biophysicists are headed in physics, chemistry and mathematics and not only biology and medicine, leading to the improvement and the development of the modern optical and electron microscopes, the generation of fluorescent probes based on small molecules or genetically encoded proteins, the preparation of synthetic oligonucleotides, the development of magnetic resonance and diffraction methods as well as computational methods. All these developments are actual tools for biophysical research. However, it was not yet clear and was therefore intensively discussed how the question “What is biophysics?” can be answered. Hill had stated that the development and implementation of physical instruments in biological research does not mean that the researcher becomes a biophysicist. A biophysicist is a researcher who investigates the biological function, organization and structure by physical and physicochemical ideas (concepts) and methods (Hill 1956). It is the way of thinking that distinguishes a biophysicist from a biologist, for instance when it is all about the importance of a quantitative, theoretically sound analysis of the problem to be investigated. In this context, the focus on theory and quantity is a central part of the methodological developments required for current biophysical research. Biophysics can be seen as a quantitative approach to the study of biological problems. In fact, biophysics, as envisioned by the four scientists in 1847, is based on the emerging convergence of sophisticated quantitative experimental analyses with computational approaches such as molecular dynamics simulations using classical and statistical mechanics to reveal the function of proteins. A major breakthrough in the field of biophysics was the development of novel microscopes that enable us to visualize structures with higher spatial resolutions that exceed the limits set by the diffraction barrier. In former times, the diffraction barrier limited the ability of light microscopes to distinguish two points separated by lateral distances of less than half the wavelength of the light used to visualize them. Another major breakthrough was the development of mostly fluorescent probes (also known as tags), which enable us to visualize living cells that can be located deep in tissue sample sections or in the tissue of living animals (Horwitz 2016). Genetically encoded fluorescent probes such as the green fluorescent protein have evolved as powerful tools for targeting specific cells or intracellular organelles, addressing problems that go far beyond chemical probes. The targeting of probes to specific cell types in a distinct tissue allows us to analyze them in living cells and tissues with high spatial and temporal resolution. To be more specific, focused light impulses can be utilized to manipulate genetically encoded targets that hence allow us to

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alter cellular functions of the whole organism. The role of the microscope has shifted from a simple tool to observe biological function to an advanced tool for modifying biological functions (Cohen 2016). This field is known as optogenetics. Finally, all these examples offer a limited overview on the importance of biophysical science. There are two concluding questions: Why do we have physical biology, biophysics and biological physics as synonyms? Or are they still characterizing different research fields? The major agreement about this issue is that biological physics studies physical problems by using biological materials such as living cells (mostly) without considering biological questions. In biophysics, researchers investigate biological problems using “trivial” biophysical approaches, in other words, devices in which the user does not need to be understood physics for answering biological questions. For example, when a scientist examines the structure of proteins, X-ray analysis does not need to be understood in detail because the computer software shows the final structure. In physical biology, however, the researcher still has biological problems, but the physics behind the biophysical device is “not trivial” and must be understood by the researcher and even the device must be improved by the researcher to achieve the right results.

1.3

Major Fields of Biophysics

Biophysics employs physical methods to investigate biological systems. The wide field of biophysics can be divided into three main branches: molecular, cellular and systems biophysics. Molecular biophysicists usually detect and manipulate single molecules in order to analyze the function of molecular motors, the process of protein folding, (bio)polymer physics, the rheology of single molecules and the dynamics of complex cellular processes such as transcription, replication and translation outside of cellular systems. Cellular biophysics unravels the molecular mechanisms inside cells or tissues such as the gating of channels, the structure and assembly of chemical networks within living cells and the imaging of fundamental cellular processes such as cell migration or cell division. Therefore, computational models are required to describe cellular processes such as metabolism and molecular switches and predict an outcome of disturbed cellular processes. The field of systems biophysics aims to describe collective phenomena and analyzes evolutionary and ecological dynamics of populations, the development of biofilms, sensory studies, neural development and signal transmission, the organization and function of synapses, the functional imaging of the brain and the coordination of movements in space and time. Biophysical research is interdisciplinary and can range from molecules, proteins, polymers, cells, tissues, organs to organisms such as animals, plants or microorganisms. In addition, viruses can also be the field of biological research. Over time, several major disciplines such as molecular and cellular biophysics, systems biophysics and subdisciplines including membrane biophysics, theoretical biophysics, biophysical chemistry and structural biophysics have been developed (Fig. 1.5). These major disciplines are all roofed under biophysics. Biophysics has developed as physically established approaches and methods applied to biological systems to gain more insights and predict the outcome of an experiment or scientific approach. It has been seen that the potential of biophysics is enormous, and the understanding of the biological subjects under investigation is necessary to further improve biophysical research. Even the field of material sciences dealing with biomimetic materials uses biophysical methods and approaches to get more insights on a special problem involving biological matter. The major breakthroughs and even fundamental improvements are urgently needed such as in the field of medicine where implants and stem cells grown on biomaterials are used.

1.3 Major Fields of Biophysics

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Fig. 1.5 Biophysics or biological physics is divided into at least these three main parts. These subdisciplines may be highly related and partly intertwined

The field of biophysics ranges from nanoscale to macroscale, dealing with widely varying time and spatial scales and even covering all aspects of the biological organization. Biophysical research has significant overlaps with many other disciplines such as biochemistry, nanotechnology, biomaterial science, bioengineering, cell biology and computational systems biology.

1.4

Molecular Biophysics

Molecular biophysics investigates biological questions similar those in biochemistry and molecular biology. The difference is that molecular biophysics tries to get more quantitative results and searches for physical substantiation of the observed biomolecular phenomena. More precisely, molecular biophysicists conduct experiments to elucidate interactions between different systems of a cell involving protein biosynthesis, replication and transcription, and how, for example, interactions between DNA, RNA and proteins are regulated. A variety of biophysical and molecular techniques, mainly derived from molecular biology, are used in the consideration of carrying out this research. In more detail, molecular biophysicists investigate the structure and conformation of biological molecules, the relationship between structure and function, conformational transitions, ligand binding and intermolecular binding, the flow of energy, thermodynamics, statistical mechanics, chemical reactions kinetics and molecular machines. In conclusion, molecular biophysics investigates physical principles that control biomolecular systems. Moreover, it aims to describe biological functions based on molecular structure, dynamics and organization emanating from single molecules and ending with supramolecular structures.

1.4.1 Subdiscipline: Structural Molecular Biophysics There are different fluorescent imaging techniques, electron microscopy, X-ray crystallography, NMR spectroscopy, atomic force microscopy (AFM) and small-angle scattering (SAS) both with X-rays

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1 The Definition of Biophysics: What Exactly is Biophysics?

and neutrons (SAXS/SANS) for the analysis of biological structures. Neutron spin echo spectroscopy can be used to observe protein dynamics. The conformation of biological substances is analyzed using biophysical and physical techniques such as dual polarization interferometry, circular dichroism, SAXS and SANS. In addition, optical tweezers or AFM are employed to investigate the forces in a molecule and to determine distances on the nanoscale. Complex biological events are seen as systems of interacting entities, which are analyzed using statistical mechanics, classical thermodynamics and even chemical kinetics such as enzyme kinetics. The structure of proteins is also modified by genetic alterations induced by specific mutations. By using molecular biological techniques, the interactions between individual proteins or complexes of molecules are also revealed. The models and experimental techniques from physics and biomathematics are applied to highly complex systems such as tissues, organs, populations and ecosystems. In particular, established and confirmed biophysical models are available for people studying electrical conduction in single neurons or neural circuit analysis in both tissue and whole brain.

1.4.2 Subdiscipline: Nanotechnology The design of synthetic DNA based on physical principles is named DNA origami. The Japanese word “Origami” means folding a plain sheet into any shape with a specific dimension. DNA origami is one of the most recent molecular biological techniques that uses DNA as a building block for the synthesis of designed nanoparticles with specific properties and functions. This novel technique is frequently employed in the field of nanotechnology. In detail, long DNA strands are folded to a complex scaffold that contains staples of DNA strands consisting of 200–300 nucleotides. Finally, a complex structure can be formed that has the desired characteristics defined by its nanoscale dimensions (Tørring et al. 2011). DNA nanostructures are not yet suitable for medical applications or the administration of drugs because they are in the early stages of development. More specifically, biocompatibility and physicochemical properties need to be determined in order to establish them as a suitable delivery tool for pharmacological drugs. In theory, DNA origami seems to have an immense potential to contribute pronouncedly to a wide range of medical fields, such as diagnostic tools for certain diseases, such as cancer, and drug delivery systems (Zhang et al. 2014). Since cancer therapy and diagnosis is such a potential filed, where DNA origami has shown significant efficacy against cancer, it will contribute to tumor oncology immensely. For example, it has been shown in an animal nude mice model system for cancer that doxorubicin-loaded, triangle-shaped DNA origami seems to be an efficient and safe manner for breast cancer treatment (Zhang et al. 2014).

1.5

Systems Biophysics

Biological science performs reductionist analysis of biological matter by using tools of biochemistry, molecular biology and structural biology and thereby reveals increasingly details on individual parts of living systems. Since the amount of molecular data is increasing, bioinformatics is required in order to sort out the relevant and general information that enable biologists to annotate, query, search and integrate these data in an easy and reliable manner. Thus, systems biology comes into play, which aims to identify similarities and reconstruct networks of the molecular signaling pathways that reveal the essential molecular biological, biochemical, biophysical and regulatory functions of cells (Fig. 1.6). In more detail, the unique properties of different cell types are required to build specialized organ systems such as central nervous, musculoskeletal and cardiovascular systems. Based on the increasingly detailed, but still yet complete, molecular network reconstructions, the basis of system

1.5 Systems Biophysics

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Fig. 1.6 Systems biology approach on the nano- and mesoscales

models of biological functions at all length scales has been created in order to simulate the dynamics of living systems under physiological or pathological conditions, which ranges from cells to organs or organisms that are displayed as large circuit diagrams of functional interactions. Major impacts have the quantitative, computer-driven approaches that enable us to gain a higher level of integrative scientific insight and to identify finally promising novel pharmacological drugs for multiple diseases (McCulloch 2016). In order to achieve the physiological function of tissues or organs, biological systems are structured in a distinct pattern and the entire architecture depends on their dynamic three-dimensional organization to ensure proper function under normal conditions. However, when the organization is disturbed, a pathological situation occurs causing possibly a diseased state. The advantage of building models is that they can be structurally integrated across the physical length scales of the organization of living matter from molecules and cells to organ systems and organisms. Similar to systems biology, the field of multi-scale modeling is becoming more and more data-intensive, but it is imperative to understand the basic principles. In order to study these different length scales of biological matter, the structural biology, microscopy and medical imaging technologies need to be combined to obtain high-quality and high-resolution datasets on molecular, cellular, tissue and organ structure length scales. However, the multi-scale modeling mainly requires physics to define and isolate molecular and cellular processes leading to tissue formation and even organ-scale physiology. The requirement for the multi-scale modeling of organ systems seems to be apparent from the viewpoint of physicians. From their viewpoint, the patients have symptoms and possess diseases that are manifested at tissue, organ and entire body scales. As the current medical treatments and drug therapies target solely specific molecules, an overall treatment approach is necessary. Hence, can we ask the question of how we can diagnose and effectively treat diseases without knowledge of the multi-scale relationships between the molecules and the whole body in order achieve the best outcome for the patient?

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1 The Definition of Biophysics: What Exactly is Biophysics?

1.5.1 Subdiscipline: Immunological Systems Biophysics Over several hundreds of million years, vertebrates were able to build a highly complex immune system in order to defend against pathogens by protective immune responses. The tolerance of this immune system to its own proteins and also to microorganisms, living in symbiosis with the host, must be guaranteed. The immune response is facilitated by the activation of regulatory networks controlled by immune cells involving distinct cytokines and cell surface receptors. All of which enables the vertebrates to initiate the precise timing of the immune response that may finally lead to the clearance of the pathogens from the host and subsequently to long-term immunity. The knowledge of the controlling of this process is regarded as the key to understand the infection process and subsequently helps to develop efficient new vaccines and immunotherapies for these infectious diseases. Therefore, the immune response should be fully understood. In more detail, we need to understand how the immune response restricts tissue damage of the host, but still works efficiently in killing the pathogens (clearance process). These two goals are archived by keeping a precise balance between effector and regulatory cells, based on immune activating and inhibitory receptors as well as gene regulatory networks. All these processes involve biophysical approaches, which seem to be highly promising in providing deep insights into immunological processes.

1.5.2 Subdiscipline: Developmental and Evolutionary Systems Biophysics During the animal development and homeostasis of animals and humans, the structure of tissues including muscles, blood or lymph vessels, and connective tissues can adapt to the external mechanical strains within the extracellular matrix. The strains can originate from the differential growth potential of various tissues, or forces caused by the contraction of muscle or by gravity. The active cell traction forces fulfill a crucial role in the alignment of cells caused by static uniaxial stretch such as muscle contraction. Using contact guidance approaches, cells manage to adjust their orientation to the cytoskeletal fibers, which usually align parallel with the strain (Vader et al. 2009; Chaubaroux et al. 2015). When the extracellular matrix is under strain such as by pulling on the matrix, the cells align parallel to the matrix fibers (Klebe et al. 1989). This behavior is termed mechanical cell-fiber feedback that drives the coordination of cellular alignment (Takakuda and Miyairi 1996; Reinhardt et al. 2013; Reinhardt and Gooch 2014) and the formation of strings along a certain strain (Dyson et al. 2016). However, in vitro observations revealed that cell alignment toward uniaxial stretch may not need to be facilitated by sole fiber alignment. Moreover, mesenchymal stem cells are able to align along the cell body orientation toward the direction of strain on a non-fibrous matrix (Liu et al. 2014). In addition, within stretched collagen fiber matrices, fibroblasts can align in parallel to the strain direction even in the absence of fiber alignment (Eastwood et al. 1998; Tondon and Kaunas 2014). In line with this, it has been found that collagen fibers align solely after the cells had aligned in a distinct manner (Lee et al. 2008; Pang et al. 2011). Moreover, fibroblasts manage to orient along the uniaxial stretch direction, although the fibronectin fibers have aligned perpendicular to the stretch direction (Mudera et al. 2000). In conclusion, these results indicate that the cells are able to orient themselves in the direction of the stretch regardless of the orientation of the fibers. In addition, the mathematical modeling seems to be a useful tool to decipher which biophysical mechanisms are suitable to explain the alignment of cells toward strain. However, the previous mathematical modeling was based on the optimization principles (Bischofs and Schwarz 2003; De et al. 2007). In more detail, it has been suggested that cells try to minimize the amount of work (energy) required for the contraction of the extracellular matrix environment (Bischofs and Schwarz 2003). For dipolar cells, the work (energy) is minimized, when these cells orient their cell axis in

1.5 Systems Biophysics

19

parallel with the uniaxial stretch. When the cells are proposed to generate strains in their local microenvironment, the cells build strings that are able to align with an external strain field (Bischofs and Schwarz 2003, 2005, 2006). Based on the finding that cells restructure their focal adhesions and stress fibers to maintain the constant local external stresses, it has been suggested that cells modulate their own contractility and orient to minimize the local stress on them in the extracellular matrix environment (De et al. 2007). It has been found that the local stress gets minimal when a dipolar cell orientates in parallel (its longest cellular axis) toward uniaxial stretch, as in this distinct configuration the cell traction forces counteract the uniaxial stretch maximally. Cellular alignment to strain can be predicted on a mesoscopic scale and hence experimentally tested to identify a cellular mechanism previously revealed by the use of computational analysis (Rens and Merks 2016). As the study of molecular evolution at the level of protein-coding genes has its limits in terms of large datasets of sequences to reveal evolutionary relationships, hence systems biophysics is required to provide an overall approach. Although a protein’s structure and conformational dynamics are crucial for protein function, the biophysical knowledge of the proteins need to be included in those studies. More precisely, biophysical constraints can provide natural selection, such as effects of protein mutations, as the physical basis for marginal stability of natural globular proteins may be required to withstand physical constraints. Moreover, altered kinetic stability may also be required and misfolding as well as misinteractions need to be avoided, as those have large effects on protein evolution and hence must be integrated, when investigating molecular evolution. In the past, the biophysical approaches have underpinned these effects, as they have been addressed by models that deal with an explicit coarse-grained spatial representation of the sole polypeptide chain. However, the sequence–structure mappings based on those models are still powerful conceptual tools to identify the mutational robustness, evolvability, epistasis, the promiscuous function of proteins performed by “hidden” conformational states, the resolution of adaptive conflicts and conformational protein switches that have evolved in the evolution from one protein fold to another. The protein biophysics science seeks to obtain more accurate evolutionary information of sequence data. In more detail, biophysical methods have also been established to get from sequence-based data, the evolutionary information to predict biophysical properties of proteins, which has thereby led to a synergy of protein biophysics and protein evolution (Sikosek and Chan 2014).

1.6

Biophysics-Related Discipline: Biophysical Chemistry

The focus of biophysical chemistry is on the functional analysis, which can be the investigation of electrogenic membrane proteins such as transporters, ion pumps or ion channels. For the analysis, electric, electrophysiological and spectroscopic biophysical methods are needed. In more detail, the proteins of interest are analyzed in their native physiological microenvironment by using methods such as voltage-clamp, patch-clamp and voltage-clamp fluorometry. In some cases, purified proteins reconstituted in artificial membranes are analyzed using various electric biophysical methods such as black lipid membranes and solid supported membranes. The two types of lipid layers build a model lipid bilayer, as they are assembled in vitro. Many modeled lipid bilayers consist of synthetic or natural lipids. A simple model system is formed by a single pure synthetic lipid. More sophisticated and hence more physiological relevant models are those made of mixtures of several synthetic or natural lipids. Different types of model bilayers exist. The first system consisted of a black lipid membrane, termed painted bilayer, which was used to electrical characterization the bilayers. It is composed of a thin layer of hydrophobic material such as Teflon having a small hole with a diameter of a few micrometers. The area around the hole was painted with a solution of lipids, which are in a hydrophobic and highly viscous solvent such as decane. After drying, a salt solution was added from

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1 The Definition of Biophysics: What Exactly is Biophysics?

both sides, and thus, a lipid bilayer is spontaneously formed. A detailed view of the transport across the membrane requires the use of membrane-kinetic methods, which allow us to investigate the transport and conformational dynamics of proteins in situ and in vitro. Bacterial transporters have been analyzed in reconstituted systems. In neuroscience and cell biology, the investigation of the function and structure of the retinalbinding protein channelrhodopsin 2 (ChR2) was of major interest. ChR2 was the first light-controlled channel and has become a brilliant tool in neurobiology, as its insertion into neuronal cells in vitro and in vivo in living animals allows cells to be stimulated simply by light. Thus, ChR2 can be used as a tool for the activation of electrically excitable cells in a non-invasive manner without vehicles, which must be absorbed by the cells and additionally leads to a known temporal and spatial resolution. Finally, this tool is widely used in basic neurobiological research and biomedical applications.

1.7

Cellular Biophysics

Cellular biophysics is a branch of biophysics that investigates cells from the perspective of a physicist. Physical methods are used to analyze cellular structures and functions and to develop models for cells based on physical and physical–chemical principles. The focus of cellular biophysics is on the investigation of living cells of different types, different organisms of eukaryotic or prokaryotic origin or various tissue origins under healthy or diseased states. Multiple studies explore the effect of gene expression and protein levels on cell mechanics. What do we currently understand about cellular biophysics? In contemplation of answering this question, we need to focus on principles, methods and certain prospects. As it is part of normal physiological conditions cells generate, transmit and can withstand mechanical forces of their microenvironment. In fact, the cells are active soft matter and are able to sense a mechanical stimulation through the activation of signal transduction pathways after a mechanosensory process activation. More precisely, cells respond to physical cues as they restructure their cytoskeletal architecture and adapt their generation and transmission of forces to the environmental needs. Genetic mutations induced by UV light or pathogens, such as bacteria or viruses, can induce alterations within the cytoskeleton of cells and hence alter the overall cell mechanics such as viscoelasticity, adhesiveness and contractile forces. All these alterations may change the cell’s behavior and function pronouncedly. In turn, alterations the cell’s microenvironmental mechanical properties can change of the cell’s cytoskeletal structure similarly and subsequently alter the entire cellular mechanics. Hence, transformations of cells are a hallmark and a symptom of certain pathologies such as cancer injuries or inflammation. There exists numerous novel experimental methods and theoretical models that have been adapted from the broad field of soft matter physics and mechanical engineering to biological living material such as cells, cell clusters and tissues. Theoretical physical models are frequently employed to characterize the cellular mechanical properties quantitatively. In addition, these physical models help to predict their alterations of cellular mechanical properties under distinct conditions. The interdisciplinary research in this special field is still growing, which is necessary to study molecular biological aspects from different angles. Together with cell mechanics, molecular and cellular biology, cellular biophysics involves approaches and physical principles in order to characterize cell mechanics and hence provides a fundamental understanding of cellular mechanical properties and processes regulating cellular development, physiology and disease, such as cancer. More precisely, besides genetic and biochemical alterations, altered cellular morphology, structural and mechanical changes may account for the invasiveness of cancer cells and subsequently lead to malignant progression of cancer such as the formation of metastases. It is now known that together

1.7 Cellular Biophysics

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with biochemical signal transduction processes, mechanical confinements or properties regulate the linear propagation of steps within the entire process of cancer metastasis including critical steps such as cancer cell migration initiation, and the spread of cancer cells from the primary tumor mass, their propagation through blood and lymph vessels and their transmigration out of the vessels endothelial cell lining into the targeted tissue for the formation of secondary tumors. Thus, it is highly important to investigate the mechanics of these steps of the metastatic cascade in cell culture systems or in animal models. The genetic and biological details of cancer have been revealed well, whereas the progress on models investigating both the biological and the mechanical parts together is rarely reported. It might be due to the lack of suitable and realistic models and biophysical measurement methods. Indeed, novel in vitro assays for studying cell and matrix mechanics and cell biological parameter simultaneously need to emerge.

1.7.1 Subdiscipline: Membrane Cellular Biophysics Membrane biophysics focuses on the fundamental features of biological membranes and the individual function of membrane proteins. Cellular membranes cover the entire cellular surface and hold the components of the cell together. Inside the cells, the membranes comprise the compartments of the cell, termed organelles, such as the nucleus, mitochondria, endoplasmic reticulum, Golgi apparatus and lysosomes. The biological membranes are composed of a lipid bilayer and membrane proteins embedded in the lipid bilayer, which acts as a barrier for ions and polar substances. The membrane barrier leads to the formation of ion gradients and electric voltages, termed membrane potentials. Therefore, membrane proteins are inevitable for the specific transport of selected proteins and substances through the membrane. In addition, the cells interact with other cells, sense their microenvironment and recognize signals, which are also performed by the membrane receptors, acting as sensors and receptors with special ligands. These signals can come from outside or inside the cell and activate the membrane receptors, which react by transducing the signal across the membrane. Moreover, membrane proteins play a central role in biological energy conversion such as photosynthesis in chloroplasts and respiration in mitochondria enabling the transport of electrons and protons over the membrane, which is the initial step of energy conversion. The membrane potentials and ion gradients are used to synthesize adenosine-5′-triphosphate (ATP), absorb nutrients, secrete waste and facilitate the flagella motor movement. In addition, even some membrane embedded enzymes can alter their hydrophobic substrates or produce hydrophobic products. The functions of the membrane proteins are in summary, firstly the passage and transport across the membrane such as passive (through channels, pores or permeases) and primary (ATP-driven) or secondary active (downhill-flow of an ion gradient) transport systems, secondly, signal sensing and transduction such as G-protein-coupled receptors, thirdly, biological energy conversion such as in mitochondria or chloroplasts and fourthly, enzymes with hydrophobic substrates or products. The function of a membrane protein such as a channel is facilitated by different mechanisms, as a channel may work by opening upon ligand binding or the ligand binding may close the channel.

1.7.2 Subdiscipline: Nuclear Cellular Biophysics Besides the cytoskeleton of the cell, the nucleus has become a focus of biophysical research, termed nuclear biophysics. The cell nucleus encloses a large number of membrane-less bodies that are crucial for the spatiotemporal regulation of gene expression.

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The knowledge about the structure and function of the nucleus seems to be essential for deciphering of the computational logic encoded in the genome on top of the sole genetic code. Hence, it is obvious that there seems to be a connection between the 3D structure of the nucleus and the nuclear biological function. The hypothesis of a strongly non-uniform spatial distribution of molecules within the nucleus does not seem to be not appropriate, however, since these variations may have a strong impact on the molecular interactions governing gene expression. It is rather more likely that the concentration variations within the nucleus are required to adapt reaction rates and regulate the diffusive transport. Diffusion is a stochastic, non-directional process, and hence, the diffusive transport rates are not dependent on the distance (as with directional transport); however, they depend on the distance-squared, which takes much more time to reach a targeted region. In conclusion, the diffusive transport seems to be a rate-limiting step for nuclear processes, especially of those in which a large number of binding partners are involved. The process of “active diffusion,” which means driven by imbalance (non-equilibrium) and ATP-dependent dynamics, can partially solve this problem, since the dynamics in the nucleus are accelerated (Weber et al. 2012; Brangwynne et al. 2008). The 3D architecture of the genome is very important for the regulation of the spatial proximity of co-regulated genes and enhancer elements, and subsequently for the timely expression of genes (Nicodemi and Pombo 2014; Branco and Pombo 2006). Finally, the establishment of efficient transport and reaction rates still remains to be identified or increased within the densely crowded nature of the nucleoplasm containing the nucleoskeletal scaffold (Matsuda et al. 2014). A special role is fulfilled by nuclear bodies as they are not membrane bound and hence pattern the concentration of molecules in the nucleoplasm. These structures can locally elevate the concentrations of molecules involved in the remodeling of chromatin, the initiation of gene transcription and the processing of RNA. Moreover, these structures seem to function as biophysical switches, as they act due to local signals to assemble only above a distinct threshold concentration. However, revealing the biophysical principles underlying their assembly and properties may help to enlighten still unknown aspects of gene regulation inside the nucleus.

1.8

Cellular Mechanics

Cellular mechanics (also referred to as cell mechanics) is a subdiscipline of biophysics. It focuses on the mechanical properties and behavior of living cells and how they are associated with cell functions such as adhesion, migration or invasion (Moeendarbary and Harris 2014). Cellular mechanics has some overlaps with cellular biophysics as it encompasses aspects of cell biophysics. In addition, cellular mechanics covers biomechanics, classical rheology, soft matter physics, mechanobiology and cell biology. Scientists working in cellular mechanics are trying to uncover the mechanics and dynamics of assemblies and structures such as plasma membranes, cytoskeleton, organelles and cytoplasm, and how these compartments interact to contribute to the emerging properties of the entire cell (Fletcher and Mullins 2010). Biomechanics research has become a focus of biophysics because the mechanical behaviors and properties of cells, cell clusters and tissues can be either a direct consequence or a regulating factor of biological function and overall cellular architecture (Fig. 1.7) (Maloney et al. 2010; Mierke 2011; Mierke et al. 2008a, b, 2010, 2017; Sun et al. 2012a). However, even both functions of the cell mechanics can be possible. Therefore, the basic goal of current cell mechanics research is to combine experimental, theoretical and computational approaches (simulations) for a realistic picture of cell mechanics behavior that can open up new perspectives on the role of mechanics in diseases such as cancer and acute or chronic inflammation (Moulding et al. 2012; Mierke et al. 2011a; Park et al. 2010).

1.8 Cellular Mechanics

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Fig. 1.7 Force scales of biological matter cover a wide range of forces

There is, however, one simple question that can be asked: What do we understand by cellular mechanics? In contemplation of answering this question, we need to focus on principles, methods and certain prospects. As it is part of normal physiological conditions, cells generate, transmit and can withstand mechanical forces of their microenvironment. In detail, the cells are active soft matter which is able to sense a mechanical stimulation through the activation of signal transduction pathways after a mechanosensory process activation. Moreover, cells respond to physical cues as they restructure their cytoskeletal architecture and adapt their generation and transmission of forces to the environmental needs. Genetic mutations and pathogens such as bacteria or viruses can induce alterations within the cytoskeleton of cells and hence alter the overall cellular mechanical, such as elasticity, viscosity and adhesiveness. All these alterations can strongly change cell behavior and function. Alterations in the microenvironmental mechanical properties of cells can also interfere with their cytoskeletal structure and consequently alter the entire cellular mechanics. Hence, transformations of cells are a hallmark and a symptom of certain pathologies such as cancer injuries or inflammation. There exist numerous experimental methods and theoretical models that have been adapted from the broad field of soft matter physics and mechanical engineering to biological living material such as cells, cell clusters and tissues. Interdisciplinary research in this field continues to grow, and it is still necessary to investigate molecular biological aspects together with cell mechanics in order to finally gain a fundamentally new understanding of cell mechanics and its importance for physiological processes such as development and pathological processes including cancer. Besides genetic and biochemical alterations, altered cellular morphology, structural and mechanical changes may account for the invasiveness of cancer cells and subsequently lead to malignant progression of cancer such as the formation of metastases. It is now known that together with biochemical signal transduction processes, mechanical confinements or properties regulate the linear propagation of steps within the entire process of cancer metastasis including critical steps such as cancer cell migration initiation, and the spread of cancer cells from the primary tumor mass, their propagation through blood and lymph vessels and their transmigration out of the vessels endothelial cell lining into the targeted tissue for the formation of secondary tumors. Thus, it is highly important to investigate the mechanics of these steps of the metastatic cascade in cell culture systems or in animal models. The genetic and biological details of cancer have been revealed well, whereas the progress on models investigating both the biological and the mechanical parts together is rarely reported. It might be due to the lack of suitable and realistic models and biophysical measurement methods. Indeed, novel in vitro assays for studying organelle, cell and matrix mechanics and other cell biological parameter, such as protein expression or intracellular localization, simultaneously need to emerge.

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1.9

1 The Definition of Biophysics: What Exactly is Biophysics?

Biophysical Bioengineering of the Cellular Microenvironment

Physical factors of the cells and the local cellular microenvironment, such as cell shape and geometry, matrix mechanics, external mechanical forces and topographical features of the extracellular matrix on the nanoscale, can all have strong influence on the fate of cell, such as stem cells. These physical factors can influence cells and the microenvironment. In particular, somatic cells can become reprogrammed into pluripotent stem cells by cell shape changes based on environmental cues (Downing et al. 2013). Stem cells sense and react to these intrinsic biophysical signals through cell– matrix receptor-based adhesions, such as integrins, maintain the balance between intracellular cytoskeletal contractile forces and deformation-resistant forces induced by the extracellular matrix. These mechanotransduction processes may be linked to several other growth factor-based signaling pathways for regulating the fate of stem cells. Various bioengineering tools, including micro- and nanoscale devices, have been successfully established to engineer the physical cues of the cellular microenvironment for specific cells, such as stem cells, and these techniques and devices have emerged as powerful tools for the identification of the extrinsic physical factors and their downstream intracellular signaling pathways regulating the functions of stem cells (Sun et al. 2012b). Central questions still arise about the molecular mechanisms with which stem cells maintain their self-renewability and facilitate their differentiation. These questions must be answered for efficient functional tissue engineering and regenerative medicine. Major efforts have been concentrated on the biochemical components and soluble factors of the microenvironment of stem cells, which are crucial for self-renewal and differentiation of stem cells. However, it has been revealed that stem cells are strongly affected by co-existing insoluble adhesive, mechanical and topological cues of the dynamic stem cell niche. Insoluble biophysical signals, such as cell shape and geometry, external forces and matrix mechanics and nanoscale topography, will induce intracellular programs controlling stem cell fate, possibly by the integrin-based focal adhesion signal and the force equilibrium across the mechanical continuum of the extracellular matrix, integrins and cytoskeleton. Since the molecular mechanisms of stem cells or similarly specific cancer cells to sense and respond to various biophysical signals are not yet known, it should be kept in mind that they can be cell specific and hence exploit distinct mechanisms that can act together. The predominant effect of specific biophysical signals on the function of stem cells relies on altered experimental settings, so that the fate of stem cells is driven by the intricate interactions and interdependences between soluble factors and insoluble biophysical signals within their local cellular environment. How can these microenvironments be engineered to reveal reproduceable results? How can interdependencies between these factors be separated? In the future, it needs to borne in mind that the development of tissues from stem cells in vivo is a long-term process in which dynamic alterations of both chemical and physical environment of the cells occur in abundance. It is currently a great challenge how we can create in vitro microenvironments for stem cells to simulate the dynamic character and sophistication of the in vivo stem cell niche. Among these microenvironments are synthetic hydrogels, microcontact printing, microfluidic systems and micro/nanofabrication of 3D scaffolds. These tools extend over widely varying length scales ranging from molecular through cellular to organ levels, all of which have demonstrated extreme performance in detecting extrinsic physical factors and their independent effects on the fate of cells. Hence, dynamics and complex synthetic cellular environments, including molecular, structural, hydrodynamic and mechanical effects, can be envisioned in combination with highthroughput approaches. Finally, there are at least five major issues that are interesting for further investigation.

1.9 Biophysical Bioengineering of the Cellular Microenvironment

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1. The fate of stem cells or cancer cells can be strongly affected by physical signals in the nearby cellular environment. 2. A key regulatory factor for the cell fate is the shape of cells that is based mainly on cytoskeletal tension. 3. The behavior of stem cells or cancer cells can be guided by nanotopographical cues that affect the molecular arrangement, organization dynamics and adhesion signal transduction processes. 4. The extracellular mechanical forces and entire matrix mechanics interfere with the behavior of stem cells and cancer cells by disturbing the force balance between the mechanical continuum of the extracellular matrix-integrin-cytoskeleton connection and its regulation by the mechanical signals inside the individual cellular niche. 5. The extracellular mechanical forces and the overall force equilibrium via the connection of the extracellular matrix-integrin-cytoskeleton are mechanically transferred into the cytoplasm and subsequently into the cell nucleus to transmit signal molecules that are essential for the fate determination of cells, including stem cells or cancer cells, as made possible by integrin signaling and mechanosensitive ion channels.

1.10

“Experimental” Biophysics is Guided by Theoretical Biophysics

Life is based on the interactions of proteins, nucleic acids, lipids and other biomolecules. The major aim is to provide detailed and quantitative predictive models of biomolecular processes such as energy conversion, molecular transport, signal transduction and enzymatic catalysis. These models help to understand the overall functions of living organisms at the molecular or cellular. The knowledge of the physical and chemical mechanisms employed in biological systems results in the development of novel technologies and biophysical devices. In more detail, computational and theoretical methods are used to explore the structure, stability, dynamics and molecular functions of single and complexed biomolecules. The computational and theoretical studies help to get more insights in the interpretation of increasingly complex experimental studies and improve the design and setup of future experiments. Theoretical biophysics combines fundamental physics, chemistry and biology to investigate biomolecular processes over a broad range. It may range from quantum mechanics to chemical kinetics, from atomistic models for physical processes and chemical reactions using molecular dynamics simulations. Finally, it can even include highly coarse-grained models of the non-equilibrium work performed by molecular machines and important for protein interactions.

1.11

Short Introduction into Biophysical Models Quantifying Cell Mechanics

Why Are Biological Cartoons and Models Needed? Biological cartoons are needed to feature the parts of an observed phenomenon that seem to be essential. Thus, conceptual models are useful to explain the outcome of a yet not known experimental setup. Basically, this approach is not restricted to the field of cell mechanics and biophysics; it is more prominent in physics as theoretical concepts for very different phenomena are described, but can be transferred all other experimental disciplines similarly in contemplation of interpretation of the data. In furtherance of understanding biochemical reactions in cells, we need to use statistical mechanics and thermodynamics. Visual schematic drawings will help to explain the important features in

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biological and biomechanical processes including the interaction between different processes. This conceptual modeling is essential to reveal the underlying processes.

1.12

Why Are These Quantitative Models Usually Idealized?

Quantitative models are an essential part of all fields of physics and are now transferred to the field of cell mechanics and biophysics. In furtherance of being able to apply appropriate models, we need a tight interplay between theoretic and experimental physics. In the ideal case, the consequences of the model are explored by performing experiments. In turn, these novel experimental results have evoked the formulation and further development of new theories. A prominent example is the famous and fundamental model of a simple spring (synonymously termed harmonic oscillator), which has been used multiple times even in various contexts. The concept of the spring is based on an old physical idea, termed the Hook’s law that has been employed initially to describe solid matter mechanics and has been combined with the mathematical idea of a Taylor series.

1.12.1 What is the Conceptual Framework? All springs have the fundamental mathematical idea in common that the potential energy for nearly every system that undergoes small displacements from its equilibrium state can be appropriately approximated by the employment of a quadratic function for the displacement. Mathematically, it can be expressed as: E ðenergyÞ ¼ 1=2 kx2 :

ð1:1Þ

The equation states that the potential energy increases with the square of the displacement x, when the system is pushed away from its equilibrium. The stiffness, k, measures what it takes drive the system out of the equilibrium and hence reflects the spring’s material mechanics. An alternative way is to characterize the springs as a restoring force that is proportional to the displacement of the spring from its position at the equilibrium state. Using mathematics, this idea can be presented in an equation: F ¼ kx:

ð1:2Þ

where F represents the restoring force, x is the spring’s displacement from the spatial equilibrium and k is termed the spring constant representing the stiffness. The restoring force is directed to x = 0, and therefore, there is a minus sign in (1.2), which indicates this and denotes the position of the equilibrium. This result is the well-known Hooke’s law. When it is stated in this way, the physical model delivers the impression of an abstract example of masses located on frictionless substrates with pulleys and springs. However, there are naturally many different examples of when this precise model can be applied. From a technological point of view, many of the most important single-molecule techniques for investigating macromolecules and their compositions evoke this description. For example, both common biophysical techniques, such as optical tweezers and the atomic force microscopy, can be precisely and unambiguously mapped to spring problems. However, there are many other problems that can be handled as a simple spring problem. In specific detail, simple spring models have been employed to describe the bending of DNA or the membrane fluctuations at the cell surface. All these processes are mechanical processes, and it is quite easy to see how the concept of a spring can work on them.

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Why Are These Quantitative Models Usually Idealized?

27

The real strength of the simple spring model, however, is that it can be applied to non-mechanical problems. For example, when we talk about biochemical reactions in a cell, we will envision how molecules move in an “energy landscape” where they cluster in “potential wells.” The mathematics of simple springs can be used to clarify our understanding of the rates that guide these biochemical reactions. Moreover, the ideas of harmonic oscillators and potential wells enlighten the investigation of conformational changes of proteins. Scientific research at the interdisciplinary interface between physics and biology requires that scientists who are educated in one discipline acquire a working knowledge of the other discipline. In the future, interdisciplinary research between physics and biology will contribute to creating a functional basis for selected key physical concepts that are directly relevant to biological problems. In addition, the identification of specific biological phenomena may help to answer important questions in the field of biology by employing classical physical equation representing each an individual specific single class of physical models and specific physical foundations, such as simple harmonic oscillator, random walks, entropy, elastic theory of one-dimensional tracks or two-dimensional sheet-like environments, diffusion, Newtonian fluid model and Navier–Stokes equation.

1.12.2 Unifying Ideas of Biological Processes Since the field of biology is highly diverse, there are so many different species, cell types, structures, genes and proteins including their mutations that it would be too time consuming to elaborate each of them one by one. Hence, simplifications need to be made in order to reveal the universal mechanism of specific parts in biology.

1.12.3 What Are the Unifying Ideas of Biology? An interesting aspect of scientific research is the connection of at the first glance unrelated phenomena. Only a few examples from the physical sciences are better able to illustrate this point than the surprising union of the ancient phenomenon of magnetism, known from the iodine stones and the beginnings of compasses for navigation to various manifestations of electricity, such as the lightning that occurs during summer thunderstorms, many and varied phenomena of light including rainbows or the apparent curvature of a spoon in a glass of water. All of these phenomena were combined by the modern electromagnetism theory. The presented toolbox of basic physical ideas concentrated on the modalities by which distinct key ideas from physics have contributed to the compilation not only of descriptive but also of predictive observations. These unifying concepts are not restricted to physical research. Moreover, biology has even far-reaching ideas. There are, on the one hand, a handful of basic physical models that are usually sufficient to create a rigorous framework for the interpretation quantitative biological datasets of different origins, and, on the other hand, there are also a handful of basic biological ideas that an encompassing basis for everything we think of in biology. In fact, many former authors have opened up various intriguing viewpoints on what might be termed the “great ideas of biology``. Feynman had chosen in this lecture on physics that one statement has the most information for future generations: It is the idea that “all things are made of atoms.” An important biological idea is the statement that “all living organisms are related through descent from a common ancestor through the process of evolution by natural selection.” Another important and clear statement has been made by Dobzhansky that is still timely and true: “Nothing in biology makes sense except in the light of evolution.” In fact, there is a strong argument that evolution is the greatest idea of biology and has left

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its inerasable stamp on almost everything. In detail, as Darwin and Wallace tried to find an understanding of the evolutionary process, Mendel worked tirelessly to establish the early ideas about the underlying mechanisms of inheritance, leading to discoveries that gave rise to another of the uniquely major ideas of biology, genetics. A major breakthrough in the history of biology was the connection of Mendel's abstract hereditary particles with the molecules of the cell, whereby the successful realization was the realization that DNA is the vehicle of the genetic information. There is a key concept of physics transferred to the field of biology where all is about cell and their interactions. More precisely, a unifying concept has been applied to cells. Stating that they are all the same, and subsequently, the cell theory has been formulated. The cell theory states that the insights of all living organisms are composed of single units, such as cells. Hence, individual cells represent the so-called quantum unit of living matter. The biochemical processes, such as common metabolic reactions in cells of several organisms, exhibit a remarkable unity or the molecular machinery that imposes our common genetic heritage is composed of a few common macromolecular compounds, such as polymerases and the ribosome. In this book, the key broad biological foundations, such as the theory of evolution, cell theory, genetics and nature of inheritance and the unity of biochemistry will be addressed.

1.12.4 Why Do We Need Mathematics? Cells are able to sense concentrations in their microenvironment and can even decide to perform a distinct action based on similar physical measurements. There are questions with many outcomes, such as whether a receptor will be bound by a ligand or not, where the probability theory can be helpful. In detail, four key distributions are mostly employed: firstly, the binomial distribution, secondly the Gaussian distribution, thirdly the Poisson distribution and fourthly the exponential distribution. Each of these distributions will contribute to sharpening the manner in which we think about many diverse biological issues. However, we will require more than simply the powerful uncertainty calculation of modern probability and statistics theory. Commonly, we seek to quantity of some specific parameters in cells, such as the number of microtubules, that varies in time. With a calculus toolkit containing differential equations, we can use one of the preferred tools that deals with the superlative mathematics, i.e., how to identify the largest or smallest values of a function. The modeling used in physical sciences can be simply transferred to biology. The interesting outcomes of the early years of electron microscopy in biology were simple, such as that, through the observation of the physical region occupied by the DNA molecule from a ruptured bacterial virus (termed bacteriophage), the genomic size can be estimated in a simple manner, which means the length in base pairs of the DNA of bacteriophage.

1.12.5 What Role Play Numbers in Cell Mechanics and Biophysics? Why is it necessary to count the copies of a molecule regulating the gene expression of a gene of interest in a bacterium such as E. coli? What is the value of estimating the rate of the polymerization of actin at the front edge of a moving cell? What is the value of knowing the time it takes a specific protein to diffuse one millimeter? Firstly, simple estimates can help to test the reality of whether our impressions of how a system works are reasonable or not. Secondly, a feel for the numbers in a system can give us an idea of what kinds of physical limitations are out there. If, for instance, we are familiar with the size of a bacterium and its swimming performance, we can determine an indicator

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Why Are These Quantitative Models Usually Idealized?

29

(termed Reynolds number) that informs us about the significance of acceleration in the dynamic phenomenon of swimming. In the same way, knowledge of the number of specific DNA-binding proteins (termed transcription factors), which govern the expression of a distinct gene, can provide information on the significance of fluctuations in the process of gene expression. Oscillatory behavior can be found in many biological processes, such as in the migration of cells, the circadian rhythm and the cell cycle process. Using mathematical models of regulatory biology, we will observe that minimal oscillators can be built from genetic networks with only two proteins, an activator and a repressor. Many of the possible parameters we could choose for protein production and degradation rates show no oscillatory behavior at all, but rather stable solutions in which the protein concentrations relax to stationary values after some time. Therefore, even a clear picture of the cabling of a network cannot tell us importantly what role this network has in the everyday life of a cell and requires a quantitative study of how this network interacts with its control parameters. In conclusion, the focus of this cellular mechanics and biophysics book is on the basic components and processes required to understand fundamental cellular functions, which are subject to biophysical, bioengineering and cellular mechanical-driven research.

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Shen, M.A. Teitell, J.K. Gimzewski, Distinct contributions of microtubule subtypes to cell membrane shape and stability. Nanomedicine 3, 43–52 (2007) A.E. Pelling, F.S. Veraitch, C. Pui-Kei Chu, B.M. Nicholls, A.L. Hemsley, C. Mason, M.A. Horton, Mapping correlated membrane pulsations and fluctuations in human cells. J. Mol. Recognit. 20, 467–475 (2007) N.O. Petersen, W.B. McConnaughey, E.L. Elson, Dependence of locally measured cellular deformability on position on the cell, temperature, and cytochalasin B. Proc. Natl. Acad. Sci. U. S. A. 79, 5327–5331 M. Prass, K. Jacobson, A. Mogilner, M. Radmacher, Direct measurement of the lamellipodial protrusive force in a migrating cell. J. Cell. Biol. 174, 767–772 (2006) M. Puig-De-Morales, M. Grabulosa, J. Alcaraz, J. Mullol, G.N. Maksym, J.J. Fredberg, D. Navajas, Measurement of cell microrheology by magnetic twisting cytometry with frequency domain demodulation. J. Appl. Physiol. 91, 1152–1159 (2001) M. Radmacher, Measuring the elastic properties of living cells by the atomic force microscope. Methods Cell. Biol. 68, 67–90 (2002) M. Radmacher, Studying the mechanics of cellular processes by atomic force microscopy. Methods Cell. Biol. 83, 347– 372 (2007)

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Radmacher, Dimensional and mechanical dynamics of active and stable edges in motile fibroblasts investigated by using atomic force microscopy. Proc. Natl. Acad. Sci. U. S. A. 96, 921–926 (1999) A.C. Rowat, J. Lammerding, J.H. Ipsen, Mechanical properties of the cell nucleus and the effect of emerin deficiency. Biophys. J. 91, 4649–4664 (2006) G. Sagvolden, I. Giaever, E.O. Pettersen, J. Feder, Cell adhesion force microscopy. Proc. Natl. Acad. Sci. U. S. A. 96, 471–476 (1999) G.W. Scarth, Colloidal changes associated with protoplasmic contraction. Q. J. Exp. Physiol. 14, 99–113 (1924) W. Seifriz, Observations on the structure of protoplasm by aid of microdissection. Biol. Bull. 34, 307–324 (1918) W. Seifriz, An elastic value of protoplasm, with further observations on the viscosity of protoplasm. J. Exp. Biol. 2, 1– 11 (1924) W. Seifriz, The structure of protoplasm, Science 1902, 648–649 (1931) W. Seifriz, M. Uraguchi, The toxic effects of heavy metals on protoplasm. Am. J. 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1 The Definition of Biophysics: What Exactly is Biophysics?

M.T. Valentine, Z.E. Perlman, M.L. Gardel, J.H. Shin, P. Matsudaira, T.J. Mitchison, D.A. Weitz, Colloid surface chemistry critically affects multiple particle tracking measurements of biomaterials. Biophys. J. 86, 4004–4014 (2004) N. Wang, D.E. Ingber, Control of cytoskeletal mechanics by extracellular matrix, cell shape, and mechanical tension. Biophys. J.66, 2181–2189 (1994) Y. Wang, D.E. Discher, Cell Mechanics, 1st edn. (Elsevier Academic, Amsterdam, 2007) R. Waugh, E.A. Evans, Thermoelasticity of red blood cell membrane. Biophys. J. 26, 115–131 (1979) S.C. Weber, A.J. Spakowitz, J.A. Theriot, Nonthermal ATP-dependent fluctuations contribute to the in vivo motion of chromosomal loci. Proc. Natl. Acad. Sci. U. S. A. 109, 7338–7343 (2012) D. Weihs, T.G. Mason, M.A. Teitell, Bio-microrheology: a frontier in microrheology. Biophys. J.91, 4296–4305 (2006) F. Zhang, Y. Wen, X. Guo, CRISPR/Cas9 for genome editing: progress, implications and challenges. Hum. Mol. Genet.23(R1), R40–R46 (2014) P.C. Zhang, A.M. Keleshian, F. Sachs, Voltage-induced membrane movement. Nature 413, 428–432 (2001)

2

Focus on Eukaryotic Cells

Abstract

This Chapter presents what we understand about soft biological matter and living biological matter. It also explores basic tools and simple models that describe biological processes at different levels of certainty. It is discussed in detail why the models for complex processes in living matter need to be simple and why they are useful. The main biological materials, the four classes of macromolecules are introduced, such as proteins that make up living cells and organisms. The general differences between prokaryotes and eukaryotes are mentioned, and the endosymbiotic hypothesis for the development of eukaryotes is presented and discussed. Cell compartments (synonymously termed organelles) are defined, and the structure and function of mitochondria are outlined. In detail, it is presented how mitochondria function by fusion and fission. In summary, the description eukaryotes, including organelles, in this chapter forms the basis for understanding of all other chapters of the Cellular Mechanics and Biophysics book dealing with eukaryotic cells.

2.1

What Is Biological Soft Matter in General?

Biological soft matter can hardly be pressed into a general definition of life and even scientists working with living matter for several years can solely approximate a broad description of life (Fig. 2.1). There exists no unique feature by which we can discriminate living matter from dead matter, whereas commonly all living systems possess certain central and general properties. In more detail, living matter systems grow, consume energy provided by their environment, reproduce to get genetically similar offspring and finally die. Scientists have tried to understand the nature of life for being able to discriminate between life and dead matter. Since both matter types consist of atoms that obey the same physical laws, however, there exist useful generalizations about the material nature of life. However, there are still differences between life and dead matter. An example for is that many molecules, which assemble living organisms, are large and structurally complex and therefore are termed macromolecules. Another example is that living organisms have large numbers of small and relatively simple molecules, such as water, metal ions and glucose, which are critical for their proper function. Moreover, these small molecules serve as building blocks to assemble macromolecules within cells. However, there exists two types of macromolecules, the first type is assembled of small molecules that can arise through non-living chemical processes, and the second type is created by characteristic © Springer Nature Switzerland AG 2020 C. T. Mierke, Cellular Mechanics and Biophysics, Biological and Medical Physics, Biomedical Engineering, https://doi.org/10.1007/978-3-030-58532-7_2

35

36

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Focus on Eukaryotic Cells

Fig. 2.1 What is the difference between living and non-living material? The living material, the crocus, (left) grows, produces and consumes energy, reproduces and dies, whereas the dead material (shrew driver) (right) cannot fulfill these tasks

biological macromolecules that are solely found in living organisms. Although the macromolecules of living matter, such as DNA, RNA and proteins, are largely diverse, the atoms building these macromolecules are mainly composed of carbon (C), hydrogen (H), nitrogen (N) and oxygen (O) and partly of phosphorus (P) and sulfur (S). The connection between the atoms building the macromolecules of living organisms is special, as they are normally not only connected by covalently bonds, and in addition by the hydrogen bonds playing a major role. Moreover, also other numerous molecules are bound to these macromolecules affecting their function and structural stability.

2.2

Four Major Classes of Macromolecules

The organisms are created by four major classes of macromolecules, such as proteins, carbohydrates, nucleic acids and lipids (Fig. 2.2). The four classes of macromolecules fulfill largely different functions within living organisms. In more detail, proteins build the structural elements of cells and catalyze specific chemical reactions in the cells and the entire organism. The membrane is formed by lipids and represents a barrier toward the exterior of a cell. Inside the eukaryotic cell, membrane lipids build their compartments, which are termed the organelles. The carbohydrates serve as energy storage, form specific membrane surface properties outside the cell membranes and built even rigid cell walls in plant cells. The nucleic acids serve as the building blocks of all kinds of macromolecules and hence are critical for the memory and the operating instructions that enable cells to replicate themselves and generate macromolecules. Each of these four classes of macromolecules have a large diversity, since they consist of numerous structurally distinct proteins. The cellular membranes can be formed by hundreds of different lipid species. The question why these four classes of macromolecules are the stuff of life cannot clearly be answered, as it would still need more knowledge about the specific chemical and physical conditions on the early Earth at the time of the first living cells’ origin. However, these molecules are prerequisites of life, as each of these four classes of molecules can be built up from a small number of simpler subunits or precursor molecules within the cell. The combination of these simple subunits to a large assembled complex provides the structural diversity that is needed to store the information about all processes in a living organism. Hence, a cell is able to synthesize with a small limited number of chemical reactions, these few simple subunits directly from the local environmental food. Proteins and nucleic acids are composed of similar simple subunits that involve the synthesis of polymers. However, different kinds of subunit types are needed to assemble the DNA and its nucleotides. The Cellular Mechanics and Biophysics book presents how special properties, such as the macromolecular behavior of proteins and DNA, depend on physical and chemical interactions. In

2.2 Four Major Classes of Macromolecules

37

Fig. 2.2 Four major classes of macromolecules

more detail, the sequential order of nucleic acid subunits manifested in the structure of cellular DNA directly encodes directly the sequence of amino acids that assemble the cellular proteins (see Chaps. 12 and 14). Finally, these observation leads to the discovery of the genetic code (see Chap. 11). The major connection between a DNA sequence and a protein sequence is based on the polymeric nature of these structures. The DNA, nucleic acids and subsequently proteins are built up from a small number of subunits. For example, the nucleic acids are composed of a simple four-letter code in which each element represents a specific nucleotide, such as adenine (A), cytosine (C), guanine (G) and thymine (T) for DNA (Fig. 2.3). In RNA, thymine (T) is replaced by uracil (U). The proteins are built up by 20 distinct building blocks and termed amino acids (Fig. 2.4). It is an extraordinary feature of living organisms that only four nucleotide bases are translated into numerous different proteins assembled by only 20 different amino acids. The important point is that the DNA bases are grouped together to small units of three following bases. All these DNA strands start with the first unique beginning of ATG for the part, which encodes the information for the protein. The following sequential bases are units of exactly three. Moreover, the three-letter code can be any combination of the four bases, in which each position is occupied by each base except the start codon and the last codon that represents the end of the gene. The so-called stop codon can have three combinations, such as TAG, TAA and TGA and are termed amber, ochre and opal (sometimes named umber), respectively (Fig. 2.5). Moreover, each possible combination of three bases (nucleotides) encodes only one specific amino acid. In turn, one amino acid is not encoded by just only one combination of nucleotides, there are others, for certain amino acids, encoding for the same amino acid, except methionine, which is encoded by solely one combination (ATG). This phenomenon is described by the Wobble hypothesis. The three-letter code builds the fundamental units of the protein’s secondary structure such as alpha-helices and betastrands. In more detail, the three-letter units are grouped together and build the gene encoding for a special protein. In some cases, the genes can be grouped together in a gene cluster, in which the genes are transcribed together. The genes in a gene cluster encode for proteins that are involved in one pathway or share a generalized similar function. For keeping the nucleic acids and proteins connected, the ribosome serves a stable mechanical linkage by taking messenger RNA (mRNA) as a template and transfer-RNA (t-RNA) as a shuttle serve to deliver the associated (encoded) amino acid for a specific three nucleotide codon, when it has the specific anticodon to bind to the mRNA. The genes and the proteins are connected through the genetic code (Fig. 2.6).

38

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Fig. 2.3 Chemical structure of the nucleotides such as adenine (A), cytosine (C), guanine (G) and thymine (T) of DNA form a double helix. The complementary nucleotides are connected through hydrogen bonds (dotted lines)

2.3

Why Are Physical Models Needed and Kept Simple?

Physical models help to get insights into the cellular mechanics and biophysics, and subsequently, these models are the goals for every biophysical scientist. Insights into cellular mechanics and biophysics are obtained by abstracting and simplifying the highly complex biological soft matter. This procedure helps to develop simple, analytical models that still predict the outcome of future experiments. As it is not possible to develop an individual atomic description for each protein, only the relevant properties of the protein are selected that are important for a specific aspect of the addressed molecule’s behavior. However, the model should not be too simple for a highly complex macromolecule, such as DNA or RNA. Hence, to deal with the enormous complexity, simple models seem to be most applicable, since they can serve as projections of the complex natural DNA or RNA molecules. Models need to be simple in order to represent most of the complex biological material. Moreover, these models are idealized by applying numerous different physical models. When a model is too

2.3 Why Are Physical Models Needed and Kept Simple?

39

Fig. 2.4 Chemical structures of the 20 amino acids building the proteins and the four major groups of amino acids

Fig. 2.5 There are three different stop codons that differ in their fidelity

specific, it may cover not many cases. Therefore, it is necessary to identify which are the essential parts that are useful for this model and what can be omitted in order to strengthen the view on the important regulatory factors. All these steps help to create an enlightening and helpful model. The view from different angles on objects or processes helps to understand the overall underlying order or structure and explains complex and diverse problems in simple steps. Finally, many diverse problems may reveal unique steps present that are present in all of them. The degree of simplicity of a model

40

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Focus on Eukaryotic Cells

Fig. 2.6 Genes and proteins are connected through the genetic code

depends on the specific question that is being studied and therefore needs to be answered. The different questions lead to various models with diverse complexity on the same issue or problem. An example of a model-like soft matter material is DNA, which is a highly important molecule, since it encodes the genetic information and has a prominent role in genetic engineering in molecular biology. Each strand of the helical DNA polymer has one polarity, which means that the two ends of the strands are different in structure and chemical composition. Thus, one end of the DNA strand is referred to as 5′ prime end and the other end as a 3′ prime end. Moreover, the entire length of the DNA molecule is heterogeneous, since the chemical identity of the bases differs from that of the neighboring nucleotide. The information given by the choice of nucleotide neighbors and their individual order in the DNA polymer carries the information of the entire genome of the organisms. These organisms can be bacteria up to humans, and still, the information of the genome is given by the order of the nucleotides and not by the molecular details of the DNA polymer itself. Thus, the focus is here to the simple sequence of the nucleotides A’s, C’s, G’s and T’s that encode the information. By knowing the sequence, the history of the mutations in the genome that led to the different species during the evolution can be analyzed. Moreover, the information within a genome can be correlated with the amount of DNA in an organism and correlated with the overall size of the species. Another example of the importance of the sequence knowledge is that variations of sequence parts can be identified as binding sequences with a strong affinity to a special protein-binding partner, such as the RNA polymerase, which copies the sequence of the DNA by building up the mRNA molecule. In addition, in certain diseases, parts of the sequence can be altered and finally be functionally linked to them. These two examples point out to similarities or changes in the chemical structure of the DNA

2.3 Why Are Physical Models Needed and Kept Simple?

41

molecule. In these two cases, the DNA is regarded as a physical entity, where the specificity of the DNA sequence is irrelevant. However, the entire mechanical and chemical properties of the molecule are analyzed, such as its total charge distribution, bending elasticity (bending modulus) or its behavior as a long polymer chain that is subject to thermal undulations in solution. Proteins can be idealized in many different manners. In more detail, since nucleic acids can be regarded as a linear sequence of nucleotides, we can also see a protein as a linear sequence of amino acids that forms the entire protein. Besides the DNA, the other main group of polymers within a cell is the group of proteins regulating all the important functions of the cell, such as the catalysis of certain chemical reactions. The precise atomic structure of the proteins is even more complex than the structure of the DNA because there are 20 instead of four basic subunits that represent proteins. In addition, the chain of amino acids has a three-dimensional (3-D) structure, which is very diverse compared to the relatively simple DNA double helical structure due to charges of the individual amino acids. From a biophysical point of view, it is solely emphasized on one specific feature of a protein, which is that a protein can be seen as a linear sequence of amino acids by omitting atomic-scale structural details and the complex chemistry including intramolecular interactions. In biology, the focus is often on a very detailed description of a material or on processes and not on a simplified description that leads to the development of models. In the linear sequence of amino acids, the hydrophobic amino acids are grouped and entitled with H. The polar amino acids are grouped and entitled with P (Fig. 2.4). The physical differences of hydrophobic and polar amino acids affect the interactions with solvents, such as water and oil, and regulate how amino acids fold and build up a functional protein. Another simplification of proteins is to present their three-dimensional structure as small cylinders, which are aligned to larger cylinders (entitled alpha-helices) and/or ribbons, which bind together (entitled beta-strands) in a precisely defined manner. Finally, the simplification helps to find out and consider the basic structural elements and their function. In order to investigate protein folding, a physical point of view on the proteins is suitable by treating them as a class of lattice models of compact polymers. In these lattice models, an amino acid is allowed solely to occupy a regular array of positions in space. The sequence of the amino acids and the folded structure of the protein provides its functions or activity. There are models simply describing the activity of the molecule. Most proteins in cells fulfill specific functions and therefore need to interact with themselves or other binding proteins. The different interactions can be modeled by treating proteins as receptors containing binding sites and others, which bind to these receptors, as binding partners (synonymously termed ligands). However, this description will omit the complex internal protein structure. Models will point on the activity of proteins and treat them as two-state systems, which can be converted into each other. One state is an active state, and the other one is an inactive state. The conversion of the states can be induced by adding ligands or structural modifications such as opening of internally bound structures by ligand binding or phosphorylation of proteins. In addition, an enzyme can catalyze a chemical reaction and can be present in two states, an active and an inactive state, which is regulated by the presence of ligands or structural modifications. These simplifications are artificial and serve to investigate the properties of a protein in isolation from the other properties. At this stage, it is simply not useful in getting solely individual information on one protein property, when the complexity of the proteins is not ignored or the sequence of the amino acids, their folding, protein compaction or conformational changes is not neglected. However, it does not mean that they are always omitted in every model. From a theoretical point of view, in some cases, it may not be useful to consider only a distinct single level of description at a time, as this leads to estimates that provide intuitive information and produces simplified models that can only serve as a first and fast overview. Therefore, we will extend our primary approach far beyond distinct macromolecules to assemblies of molecules on a larger

42

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Focus on Eukaryotic Cells

scale, such as cellular membranes, which consist of large numbers of lipids and proteins. The views on the membrane are different and depend on whether biologists or physicists are the observers. If we have distinct macromolecules, large-scale analysis may be useful by focusing on a single aspect of their physical behavior. In more detail, the membranes can be seen as bendable and spring-like (elastic) sheets, random surfaces or as arrays of electrical elements such as a capacitor or resistor. During the interaction between two cells, membranes represent selective barriers through which distinct molecules can pass, whereas others are hindered in their motion. The same is true for entire cells migrating through a dense and closed (confluent) cell-layer barrier. The degree of idealization of the biological membrane depends strongly on the individual questions that we have asked us at the beginning of the experimental approach. The idealization can be transferred from plasma membranes to entire living cells. A living cell is highly complex, and we need to extract the essential physical property of the cell to get insights in particular functions by using simple approaches. One example organism is the bacterium Escherichia coli, which can be seen as an object with many protein receptors on its cellular surface. A bacterium can swim through a water-based environment, and hence, it can be regarded as an elastohydrodynamic object, which is entirely a mechanical object being bendable and interacting with the flow of the water-like fluid. In more detail, the physical properties of the distinct path the bacterium follows can be modeled by considering the bacterium’s large-scale motion representing a biased random walk, which simply ignores all the hydrodynamic details contributing to bacterial motion. As biological research is more focused on alterations in expression of polymers due to environmental conditions, it may be useful to treat a cell as a device processing information such as the cell is regarded as a network that regulates the flow of information through gene expression. This view is not exclusive, and abstractions have been made to improve the clarity. The abstraction concentrates on a single physical property to describe the biological entities, which is also currently used for consideration of other systems such non-living soft matter that include solutions of charged ions. There exist various abstractions of water being the medium of life and a solution representing regular lattice structure. In order to investigate ligands in a solution and their ability to bind to a receptor, the solution is treated as a liquid in which are only discrete positions permitted for the ligands. However, this is an extreme approximation, which is valuable for calculations that involve the chemical equilibrium. Indeed, it turned out that this model is extremely valuable for calculations involving the chemical equilibrium and delivers remarkably accurate quantitative predictions. Although it is still a huge approximation, it is helpful for calculations about the chemical equilibrium of reactions, as it provides exact quantitative predictions. When living matter interacts with water, the hydrodynamic flow properties are included. When we calculate the chemical reaction rates in water solvents, we need to consider the diffusive fluctuations, since the macromolecules in the cytoplasm of cells, which mostly consists of water, are altered by the specific properties of water. The chemical groups on proteins are often polar or hydrophilic (love water and hence attract water), which can form hydrogen bonds with water, whereas other groups are oil-like and hydrophobic (hate water and hence repel water). In folded proteins, the oil-like groups usually cluster inside of the globular protein; however, charged surface-membrane located molecules can interact with the dielectric property of water molecules, which means that that are neutral in principle, but have a slight charge separation similar to dipoles. All these simplifications and reductions help to answer many different biological questions and formulate future models. However, none of these simple models will answer all questions and lead to a broad understanding of the diverse behavior of living soft matter such as cells. Each model seems to reveal an insight into certain aspects of living cells, and finally, all these models can help to provide a realistic overall picture of the cells.

2.4 Characteristics of Prokaryotes and Eukaryotes

2.4

43

Characteristics of Prokaryotes and Eukaryotes

All cells share four components, such as DNA, plasma membrane, ribosomes and the cytoplasm. The DNA contains the genetic information of the cells, at the ribosomes is the genetic information translated from RNA to proteins, the jelly-like solution of the cytoplasm is termed cytosol and the plasma membrane encompasses the entire cell to separate it from its environment. There exist two major types of cells that are referred to prokaryotes and eukaryotes.

2.4.1 Prokaryotic Cells Besides the four similar components, prokaryotes are different from eukaryotic cells in several respects. A prokaryote can be described as a simple, unicellular organism that contains neither an organized nucleus nor any other membrane-bound organelle, which are the major differences to eukaryotes. The DNA of prokaryotes is located in a central part of the cell, which is termed the nucleoid. In general, prokaryotes are surrounded by a peptidoglycan cell wall, and many prokaryotes possess a polysaccharide capsule. In more detail, the cell wall serves as an additional protective layer for the cell’s interior that supports the maintenance of the cell’s shape and helps to omit dehydration. The cell capsule facilitates the attachment of the cell to surfaces in its local microenvironment. In addition, certain prokaryotes possess flagella, pili or fimbriae on their cell surface. Prokaryotes utilize flagella for movements. The pili can be employed to transfer genetic material from one cell to another cell by direct cell–cell contact during a distinct type of reproduction, which is termed conjugation. Prokaryotic fimbriae can be exploited by bacteria to interact with a host cell. A commonly investigated simple cell model for the prokaryotic cell is the bacterium Escherichia coli (E. coli) (Fig. 2.7). E. coli lives in the gastrointestinal tract and serves as a model organism for the investigation of bacterial cell motility and the specific function of the flagella from a biophysical point of view. In molecular biology, E. coli is used as a laboratory organism helpful for genetic engineering.

Fig. 2.7 Simple structure of the bacterium E. coli

44 Table 2.1 Differences between prokaryotes and eukaryotes

2

Focus on Eukaryotic Cells

Prokaryotes

Eukaryotes

Ribosomes

70S

80S

Nucleus

No

Yes

Chromosomes

No

Yes

Histones

No

Yes

Alternative splicing

No

Yes

Compartments (organelles)

No

Yes

Zygote (spores)

No

Yes

S = Svedberg unit

The main differences between prokaryotes and eukaryotes are listed in Table 2.1. The Svedberg unit (s) is the sedimentation rate for a particle, such as a ribosome, of an individual size and shape, and shows how fast a particle has moved to the bottom of a tube, when a centrifugal force is applied using a centrifuge. For example, a particle with a sedimentation coefficient of 70S (70  10−13 s) has a sedimentation speed of 30 microns per second under the influence of an acceleration of 107 m/s2 (a million gravities). The Svedberg unit depends on the size, shape and crosssectional area of the particle, such as cell or organelle, and is a measure of time. It is affected by the frictional force on this particle during the sedimentation process.

2.4.2 Eukaryotic Cells Prokaryotic cells have commonly a diameter in the range of 0.1–5.0 lm, and hence, they are pronouncedly smaller than eukaryotic cells, which cell diameters are in the range of 10–100 lm. Since prokaryotic cells are small in size, ions and organic molecules enter them to rapidly diffuse into all other parts of the cell. Likewise, any waste produces in a prokaryotic cell rapidly diffuse out of the cell into the local environment. In contrast, eukaryotic cells have utilized different structural architectures and mechanisms to perform efficient intracellular transport. However, for all cells encompassing prokaryotic or eukaryotic cells, a small size is envisioned. Since most cells exhibit a spherical shape, they can be approximated as a sphere, with a surface area of 4pr2 and a volume is 4/3pr3. As the cell radius increases, its surface area increases as the square of its radius, while its volume increases the cube of its radius (much faster). Hence, as the cell size increases, the ratio of the surface area to volume of the cell decreases. In addition, when a cell grows too strongly, the plasma membrane does not have enough surface area to sustain the diffusion rate demanded for the increased volume. Hence, as a cell grows, it becomes less efficient. In order to overcome this less efficient state, the cell needs to divide. Another option to improve efficiency is to develop organelles that perform distinct tasks. These modifications resulted in the development of more complex cells known as eukaryotic cells. Moreover, these eukaryotic cells are structurally highly complex at a nanoscale and mesoscale level, can grow in clusters and arrange to higher ordered structures such as tissues at a microscale level. These ordered structures are specialized compartments that are termed organelles. Moreover, different organelles fulfill diverse roles within the cell. For example, mitochondria generate energy from molecules that serve as food, such as glucose, lysosomes degrade and recycle organelles and biomacromolecules and the endoplasmic reticulum rebuild membranes and facilitate the protein transport throughout the entire cell. However, there is one central question that needs to be raised here: What are the characteristics that all organelles have share?

2.5 Organelles—What Defines Them?

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Organelles—What Defines Them?

The cellular interior of eukaryotic cells is rich of organelles. The extraordinary size of eukaryotic cells compared to bacterial cells causes the compartmentation of cells into closed structural units such as the organelles. One major parameter that distinguishes bacteria and archaea from eukaryotes is the membrane confined organelles (Fig. 2.8). Besides the cell nucleus, eukaryotic cells possess several other types of organelles including mitochondria, Golgi apparatus, endoplasmic reticulum (ER), chloroplasts (in plants) and lysosomes. Organelles are specialized compartments in which the cell carries out specific functions, such the management of the genome (in the nucleus), energy generation (in mitochondria and chloroplasts), protein biosynthesis and the posttranslational modification of proteins (in the ER and Golgi apparatus). The organelles are cellular compartments confined by phospholipid membranes possessing completely different protein and ion compositions compared to cellular membranes. Moreover, the membranes of each of these different membrane systems, which enclose the organelles, possess a specific composition of lipids and proteins. Each individual organelle fulfills a distinct function that is crucial for the survival of the cell. Almost all eukaryotic organelles are surrounded by a membrane that separates the interior of the organelles from the cytoplasm. These organelle membranes are lipid bilayers that are highly similar to the outer plasma membrane of the cell. Hence, a characteristic feature of many organelles is that they are compartmentalized structures, which are separated from the rest of the cell by membranes. Similar to the plasma membrane, the organelle membranes hold the inside “inside” and the outside “outside.” This division allows different types of biochemical reactions to take place in different organelles and additionally protects the cytoplasm from dangerous reactions. Although each organelle has its own specific function in the cell, all cell organelles can work together in an integrated way to cover the overall needs of the cell. An example are biochemical reactions in the mitochondria of a cell that transfer energy from fatty acids and pyruvate molecules into a highenergy molecule, such as adenosine triphosphate (ATP). The rest of the organelles of the cell then use this ATP as an energy source for operation. An advantage of these membrane surrounded compartments is that they can be easily visualized, for example, by high-resolution electron microscopy of a thin cross section or slice of a cell. Thereby,

Fig. 2.8 Typical basic structure of a cell containing several membrane confined organelles

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Fig. 2.9 Fluorescent microscopic image of a mouse embryonic fibroblast. The F-actin cytoskeleton is visualized through AlexaFluor548 Phalloidin (red), and the nucleus is stained with Hoechst 33,342. The scale bar is 10 µm

structural details and key characteristics of different organelles, such as the long, thin compartments of the ER or the compacted (condensed) chromatin of the nucleus, can be identified. Since a cell is dynamically remodeled, its organelles are not static and hence undergo constant motion. Sometimes, the organelles move even to a certain location within the cell, merge with other organelles and become larger or smaller. The nucleus is one of the well-known examples, as it is often easily visible with standard light microscopy. When we use fibroblasts as an example, the cell has dimensions of about 50 lm, while the nucleus has a characteristic linear dimension of about 10 lm (Fig. 2.9). From a functional point of view, the nucleus has a lot more to offer than merely serving as a reservoir for the genetic material. In the nucleus, the chromosomes are highly organized and hence create specific domains, which is discussed in detail (see Chap. 10). Since transcription and various kinds of RNA processing occur in the nucleus, molecules, such as transcription factors, fluctuate in and out the nucleus, while completed RNA molecules are exported out through gateways within the nuclear membrane, known as nuclear pore complexes. The parts of the genome that is involved in synthesis of ribosomal RNA are clustered together in a specific compartment. This compartment is characterized by striking spots, which can be seen in the light microscope, and they are referred to as nucleoli. When molecules are transported out of the nucleus, they impinge the next membranous organelle, which is referred to the endoplasmic reticulum. In fact, the membrane of the ER is connected with the membrane of the nuclear envelope. In certain cells, such as pancreatic cells, the endoplasmic reticulum occupies most of the cell interior. This complex organelle serves as the site of lipid synthesis, and also, the site of synthesis of proteins extended to be excreted or incorporated into membranes. The endoplasmic reticulum can acquire various geometries in diverse cell types and under varying conditions. There remain still not yet answered questions: How much total membrane area does the ER occupy? How heavily does the special membrane morphology influence the overall size of the organelle?

2.6 Endosymbiotic Hypothesis

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Endosymbiotic Hypothesis

Symbiogenesis basically means living together and points out to the decisive role of symbiosis in evolution, which contributes to important innovations. The term is usually earmarked for the major transition from prokaryotes to eukaryotes, such as the photosynthesis of eukaryotic algae and plants through the process of endosymbiosis. In some eukaryotic lineages, however, endosymbionts have been secondary lost, indicating that symbiosis can lead to a major evolutionary innovation even under this rare circumstance. This finding leads to the compelling assumption that symbiosis has played an important role in other major evolutionary innovations, even if not all existing representatives of such groups still have the symbiotic link (Gray 2017). In 1967, the endosymbiotic theory of organelle origins has been formulated (Sagan 1967) and is now widely accepted (Archibald 2014). The endosymbiotic theory found that three fundamental organelles such as the mitochondria, the photosynthetic plastids and the (9 + 2) basal bodies of flagella were once free-living prokaryotic cells (Sagan 1967). The fact that mitochondria and plastids could endosymbiotically originate from prokaryotic ancestors was not a new idea at that time, which only emerged in various forms at the end of the 19th and beginning of the twentieth centuries before fading out from a biological point of view (Sapp 1994). Therein, it has been stated that the article in 1967 was remarkable in that it presented an all-encompassing view of endosymbiosis as the endpoint of the eukaryotic cell, and hence, this theory seems to be the first coherent theory of eukaryogenesis. More precisely, a third subcellular structural element, the eukaryotic flagellum (formerly termed undulipodium), was proposed to originated from the endocytosis of specific motile prokaryotes, such as spirochaete-like prokaryotes, which may then get connected by symbiosis to their cellular hosts. This overall scenario was later referred to as the serial endosymbiosis theory (Taylor 1974). In contrast to the proposed endosymbiotic origin of mitochondria and plastids, there seems to be no evidence of the origin of mitosis (Sagan 1967). Since despite efforts to identify one, no genome was linked with the eukaryotic flagellar apparatus (Johnson and Rosenbaum 1991). The genomes of the mitochondrion and the plastid or more precisely the genes they house them and how they are arranged and expressed enable us to know with a high degree of certainty where these organelles come from: the bacterial species a-Proteobacteria and Cyanobacteria (Gray and Doolittle 1982; Gray 1992). The energetic promotion of the role of symbiosis in eukaryotic cell development (Margulis 1970a, b) triggered a lively debate about autogenous origin (origin from inside) and xenogenous origin (origin from outside) theories of organelle development. At that time, the endosymbiont scenarios for both mitochondria and plastids were controversially discussed (Uzzell and Spolsky 1974), with the concentration in this period being primarily on the mitochondrion (Raff and Mahler 1972). The mitochondrial genetic system was reported to possess a great inter and intraspecies diversity, which leads to the hypothesis that it is a unique system distinct from prokaryotic and eukaryotic cells (Mahler 1981). The comparative analysis of mitochondrial genomes revealed that support for both scenarios endosymbiotic or no endosymbiotic event can be found (Burger et al. 2003). Biochemical, molecular and cell biological findings and the characterization of a group of eukaryotic microbes, the jakobid flagellates, as a gene-rich mitochondrial genome that strongly resembles a reduced bacterial genome (Burger et al. 2013) and thereby delivers an exemplary case for a single,endosymbiotic, aproteobacterial origin of mitochondria, are all indicators for an endosymbiotic origin (Gray et al. 1999; Gray 2012). However, a compelling case for an endosymbiotic origin is easier to find for the plastid than for the mitochondrion, since the plastid is evolutionarily younger than the mitochondrion. The last eukaryotic common ancestor had still a functional mitochondrion that comes close to its modern counterpart (Koumandou et al. 2013), and several major eukaryotic lineages, containing animals and

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fungi, are clearly primitively aplastidic and originate from ancestors who never had plastids. Therefore, the similarity between plastic and cyanobacteria structure and biochemistry is much more pronounced in most plastic-containing eukaryotes than in the mitochondria/a-proteobacteria comparison. In addition, plastid genomes usually contain considerably more genes on which such a comparison can be based than do mitochondrial genomes, and hence, the plastid translation system exhibits more bacterial character than its equivalent in most mitochondrial systems (Gray 1992). The treatment of the plastid has been in that way that eukaryotic plant cells did not develop oxygen-eliminating photosynthesis. However, these cells become photosynthetic active by symbiosis with blue-green algae, such as cyanobacteria (Sagan 1967). More precisely, various photosynthetic eukaryotes (protoplastids) were recorded at different times during the evolution of heterotrophic protozoans that were transformed into obligately symbiotic plastids and persevered their typical photosynthetic pigments and pathways (Sagan 1967; Raven 1970). However, the current consensus for the development of the eukaryotic line, which includes both land plants and green, red and cyanophytic alga, is a single, separate, endosymbiotic origin of mitochondria and plastids, whose primary origin from an endosymbiotic cyanobacterium lies in an ancestor of Archaeplastida. Plastids then penetrated other algae clades via a secondary symbiosis process in which a eukaryotic host takes up a eukaryotic symbiont (a green or red alga) (Archibald and Keeling 2002). Although there is agreement that mitochondria and chloroplasts are obtained from free-living bacterial ancestors through an endosymbiotic process, however, the mechanism still not clearly revealed. In the case of the mitochondrion, it is still unclear when the triggering event occurred related to the origin of the eukaryotic cell and how long the process of converting bacterial endosymbiont into fully integrated organelles took and by what mechanisms. In fact, in over five decades since the first publication on the theory, a variety of symbiogenetic models have been formulated that arouse various hosts and processes (Martin et al. 2015). A particularly controversial aspect is the nature of the host. In endosymbiosis, the host is often represented as a primitive and amitochondrial eukaryote, which captures a prokaryotic symbiote through phagotrophy, which is a process of capturing and internalizing other organisms through phagocytosis. Other descriptions show the host as a prokaryote taking another prokaryote by phagocytosis, although the process of phagotrophy is still unknown in free-living prokaryotes. The first step in the development of eukaryotes from prokaryotes was driven by the survival in the new oxygenated atmosphere: An aerobic prokaryotic microbe, such as a protomitochondrion, was incorporated into the cytoplasm of a heterotrophic anaerobic organism. The process of endosymbiosis seems to be mandatory and led to the development of the first aerobic amitoid amoebae. It remained unclear whether the host was a prokaryote or an advanced version of it. Based on ameboid and ingestion, a type of protoeukaryote can be hypothesized, although it still lacks a nucleus and mitotic apparatus, which are both characteristic features if a eukaryote. However, also a prokaryotic host seems to be possible (Margulis 1981). Moreover, protomitochondria may invaded their hosts similar to modern predatory bacteria, such as Bdellovibrio that can even be performed without phagocytosis. In fine detail, Bdellovibrio can effectively destroy its host (the bacterium) during the invading process. However, it remains still an open question of how this route may develop into a stable prokaryote–prokaryote symbiosis. A central point in various symbiogenesis models of mitochondrial origin and evolution is the nature of the host, which is divided into two major types: These are the mitochondria early type (termed mito-early) and secondly mitochondria late type (termed mito-late), which can be distinguished in timing of the transition from first eukaryotic common ancestor to last eukaryotic common ancestor and with different effects on the entire eukaryotic cell origin (Poole and Gribaldo 2014). Based on comparative genomics, it can be underlined that the last eukaryotic common ancestor A was quite a complex organism with a completely functional mitochondrion (Koumandou et al. 2013) and

2.6 Endosymbiotic Hypothesis

49

that all allegedly mitochondrial eukaryotic lineages, except one (Karnkowska et al. 2016), comprise mitochondrion-related organelles and originate from mitochondria-containing ancestors. Thus, the initial acquisition of a bacterial symbiont designated to form the mitochondrion could not have taken place very close to the emergence of the last eukaryotic common ancestor because of the numerous and complex alterations that appeared during the transition from symbiont to organelle, although evidence for a late mitochondrion acquisition has been reported (Pittis and Gabaldón 2016). Probably, the most famous mitotic early model is the hydrogen hypothesis (Martin and Müller 1998), where the host, an anaerobic, hydrogen-dependent archaeon, picks up an a-proteobacterium that is able to respire but produces molecular hydrogen as a waste product of the anaerobic heterotrophic metabolism. The selection of endosymbiosis is pushed forward here by metabolic syntrophy between the two partners: The waste product (hydrogen) of one partner is an essential metabolic resource for the other. Both mitochondrion and eukaryotic cell origin are simultaneous in this scenario, with the subsequent subcellular characteristics of the latter being directly dependent on a distinctive increase in cell energy. In contrast, in mito-late models, the underlying mechanism of symbiogenesis is phagotrophy, a hallmark of eukaryotic cells that is widespread within the eukaryotic domain. Phagotrophy is an endocytosis in which the boundary membrane of one organism (host) surrounds another organism (symbiont) and thereby internalizes it in a membrane-bound phagosome (Fig. 2.10). In a number of mito-late models, the host is virtually an amitochondriate eukaryote with the potential for phagocytosis (Cavalier-Smith 1987). Eukaryotes are closely related to a new group of Archaea, the Asgard superphylum (ZarembaNiedzwiedzka et al. 2017) that encodes numerous proteins whose homologues have so far only been detected in eukaryotes. Hence, it can be hypothesized that this strain represents an archaeal line, which had originally developed characteristic eukaryotic features, such as the process of phagocytosis, and therefore seems to be the initial host for mitochondrial endosymbiosis. These observations form the basis for the phagocytotic archaeon model of eukaryogenesis, where the mitochondrial endosymbiont was derived by a temporarily complex phagocytotic archaeon (Martijn and Ettema 2013). The transition from symbiont to organelle obviously entailed many steps: The dispersal of the bacterial cell wall, early capture of key metabolite transporters by the symbiont, substantial and

Fig. 2.10 Endosymbiosis

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variably reduction of the symbiont genome by total loss or transfer of genes to the nucleus, functional activation of transferred genes in the nucleus and retargeting of their cytoplasmically synthesized proteins back into the developing organelle based on specific organellar targeting sequences or anywhere else in the cell and large-scale recruitment of many supplemental organellar proteins whose origin is obscure. After all, this transformation process seems to be a step-by-step process over a wide period of time. Although much knowledge has been gained, great efforts are still needed to determine the origin of the mass of the mitochondrial and plastid proteomes, since they seem to have at the first glance neither a-proteobacterial nor cyanobacterial origin. While it is generally accepted that mitochondrion and plastid are the result of bacterial endosymbiosis, however, these organelles consist of mosaic components and functions, which have more than one origin (Cavalier-Smith 1987). In conclusion, symbiosis fulfills a crucial role in organelle origins and the entire eukaryogenesis, but its role is not all-encompassing.

2.7

Single or Serial Endosymbiosis

Does the endosymbosis process occur as a single event or are serial endosymbiosis events possible? Indeed, there is a new concept discussed that predicts serial endosymbiotic events and is hence termed serial endodymbiosis theory (Fig. 2.11) (Archibald 2015). There are many roots of the endosymbiotic theory, and some of tehm are even intertwined. It begun in 1897 with the concept of endosymbisos hypothesized by the Swiss biologist Simon Schwendener, which states that lichens were a composite being composed of a fungus and an alga (Honneger 2002). For systmatists of the nineteenth century, lichens did not fit into the classification schemes (Sapp 1994). In 1879, symbiosis was described as a combined living of two different organisms of different types by the German Anton de Bary (de Bary 1879). This statement was supported by the pioneering work of Polish Franz Kamienski and the German Albert Frank on mycorrhizal fungi, which posses an intimate connection to the roots of plants (Berch et al. 2005; Frank and Trappe 2005). At that time, symbiosis was recognized as a legitimate, yet astounding, biological phenomenon. Thereby, the Russian botanist Constantin Mereschkowsky fulfilled a major role the development of the symbiogenesis concept development, which can be seen of the generation of a new organism from two or more formerly independent organisms that are now interacting to grow and survive together (Mereschkowsky 1920). Mereschkowsky was familiar with subcellular architecture of diatom algae and their chromatophores (synonymously termed plastids) and in lichen biology (Sapp et al. 2002). Hence, in a 1905 publication that has been later translated into English by Martin and Kowallik in 1999 (Martin and Kowallik 1999), Mereschkowsky pointed out that this is the origin for the endosymbiotic plastids. Mereschkowsky reviewed in this pioneering work the present state of knowledge with respect to symbiosis, including lichens and certain amoebae containing green algae living inside their cytoplasm. Therein has been stated that there are obvious similarities between cellular plastids and freeliving cyanobacteria. In addition, it has been pointed out that there exists a continuity of chromatophores in cells by the German Andreas Schimper and the Swiss Carl Wilhelm von Nägeli. Hence, they and others concluded that the plastids are no de novo products of the cell, and they originate rather from other preexisting organelles by the event of organelle division. In line with these results, Mereschkowsky strongly proposed that the plastids had in former times been individual freeliving organisms, and similarly, it has been reported by Schimper (1883). Hence, Mereschkowsky is mainly seen as the founder of the famous endosymbiotic theory. However, it has not been noted that mitochondria might be originated by endosymbiosis, although the foundation for the hypothesis of endosymbiosis had been started (Sapp 1994; Archiblad 2014). In the eighteenth century, the concept

2.7 Single or Serial Endosymbiosis

Fig. 2.11 Single or serial endosymbiosis

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of symbiosis was forgotten and not mentioned as possible source with great evolutionary capacity. The concept of endosymbiosis was once again put in the spotlight by the American biologist Lynn Margulis (Sapp 1994; Archiblad 2014; Margulis 1998). In the 1950s and 60s, she investigated algae and amoebae and thereby hypothesized that there is properly extranuclear DNA in eukaryotes (Plaut and Sagan 1958). In 1962, there was the first reported microscopic observation for DNA that is located in plastids (Ris and Plaut 1962) Moreover, the same was identified for mitochondria (Nass and Nass 1963). Since knowledge from different disciplines, such as genetics, bacteriology, cell biology, ecology and even paleontology was gained, Margulis was able to publish a bold, broad-ranging hypothesis for the overall evolution of eukaryotic organisms (Sagan 1967). Therein, the origin of mitosing cells was proposed to be originated from the uptake of free-living cells to create as an ancient endosymbiosis event that then become mitochondria, the 9 + 2 basal bodies of flagella and photosynthetic plastids (Sagan 1967). In 1970, a book entitled “Origin of Eukaryotic Cells by Margulis came out (Margulis 1970a, b) and shed light on symbiosis and especially on endosymbiosis. Thereafter, in the molecular biological age, these ideas became the focus of scientific biological research (Archiblad 2014). Besides Margulis, other researcher reported the importance of endosymbiosis for the evolution of eukaryotic cells (Goksøyr 1967; Raven 1970). Is there an alternative way for how organelles may be developed? Another view on this topic was that mitochondria and plastids originated within the confinement of a photosynthetic eukaryotic cell developed from a cyanobacterial-like prokaryote (Klein and Cronquist 1967; Cavalier-Smith 1975). In contrast, Margulis did not support this hypothesis (Margulis 1981). In contrast, she explained that oxygen-containing photosynthesis is not an ancestral eukaryotic characteristic, but has only recently developed through endosymbiotic incorporation of a cyanobacterium by an a heterotrophic eukaryote. Other model for the origin in of the oxygen-containing photosynthesis or mitochondria came from report by Lawrence Bogorad (1975), Rudolph Raff and Henry Mahler (1972) as well as Uzzell and Spolsky (1974) that are highly similar to Klein Klein and Cronquist, since they suggest that the intracellular compartments have developed through an mechanism by which finally membrane-bound mitochondria and plastids had formed. All these reports present diverse assumptions about the specific type of prokaryotic population that produced the earliest eukaryotic cells. All these models agreed that endosymbiosis is needlessly radical, since they argue that there is no obvious reason why the initial eukaryotic cell, capable of remarkable evolutionary innovations, should have emerged as a composite of procaryotic cells and cell parts, rather than developing more directly from a partly prokaryotic cell type (Raff and Mahler 1972). In 1974, Max Taylor postulated a so-called Serial Endosymbiosis Theory that provide a framework for all hypothesis, which at the first glance seem to be competing. Thereby, he developed the “autogenous” and “xenogenous” (synonymously termed foreign) models for the eukaryotic evolution, and subsequently, the latter model was the fundament for the “Serial Endosymbiosis Theory” (Archiblad 2014; Taylor 1974). In the mid-1970s, the first nucleic acid sequences have been identified that support the endosymbiont hypothesis using the laborious RNA cataloguing technique invented by Carl Woese that has been employed to identify the archaea (Woese and Fox 1977). Linda Bonen, Ford Doolittle, Carl Woese and colleagues received extracts of rRNA sequences from algae plastids and cyanobacteria and thus showed a strong evolutionary connection for them (Bonen and Doolittle 1975, 1976; Zablen et al. 1975, see for further reading: Archiblad 2014; Sapp 2009). The rRNA fragments of the mitochondria were confirmed to originate from prokaryotes (Bonen et al. 1977). However, in 1977, these rRNA fragements have not been associated with distinct bacterial lineage, as a phylogenetic linkage between a proteobacteria and mitochondria has been found in 1985 bei Carl Woese and collegues (Yang et al. 1985). The hypothesis of Margulis stating that eukaryotic flagella (synonymously undulipodia) originated through endosymbiosis (Sagan 1967; Margulis 1970a, b, 1981)

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could not be experimentally examined directly, since DNA was not detected in these organelles providing active cell movement. Moreover, the evidence of the endosymbiont hypothesis about the of mitochondria and plastids was a conclusion. It has been argued that the molecular sequencing techniques have improved greatly and hence it has been found that mitochondrial and plastid rRNA sequences are more similar to those of specific bacteria (different one for each organelle) than they are the their own nuclear rRNAs. It was the contemplation of a plethora of insights into the biochemistry and molecular biology of organelles accumulated by researchers worldwide that culminated in the death of the autogenic model. In the mid-1980s, it turned out clearly that endosymbiosis seems to be the only explanation for the origin of the organelles (Gray 1992; Gray and Doolittle 1982).

2.8

How Can Eukaryotes Lose Their Mitochondria?

The acquisition of mitochondria enabled eukaryotes to overcome the limits of their structure and hence developed complex organisms (Lane 2017). More precisely, the eukaryotes were able to possess new gene families, and in the last eukaryotic common ancestor, 3000 of these new gene families have been detected (Koonin et al. 2004; Fritz-Laylin et al. 2010). There were even larger proteins (Brocchieri and Karlin 2005), and higher gene expression levels recognized in this ancestor. Moreover, eukaryotes accumulated far more DNA including regulatory proteins (Elliot and Gregory 2015), which at the same time does not indicate that eukaryotes possess an enlarged genome. Instead distinct eukaryotes preformed genomic streamlining, which had never been reported for prokaryotes. 15 Mb is the largest size of the genome in bacteria and archaea, which is 10,000-folds less than the largest eukaryotic genome sizes that can be up to 150,000 Mb (Elliot and Gregory 2015). This large difference in size cannot be explained by a simple continuum of complexity between prokaryotic and eukaryotic cells (Lynch and Marinov 2015, 2017; Lane and Martin 2016). When mitochondria make the difference in complexity, how can eukaryotic cells remain such complex cells if they have lost mitochondria secondarily? The answer refers to amount of pressure for the selection. In bacteria, the advantage is little or no if you are a little bigger, have a little more membrane and more ATP; these cells tend to lose to smaller, more streamlined cells that multiply faster (Vellai et al. 1998). Since bacteria produce enough ATP, more ATP would be no or only a little improvement, which is most prominent in obligate fermenters (Lane 2011, 2015). More precisely, these bacterial fermenters have to be competitive with other cells that can generate more energy from the same substrate and thus grow longer. Moreover, the obligate fermenters among bacteria need to compete with others by growing and multiplying rapidly and are therefore usually among the smallest and most genomically streamlined cells (Makarova and Koonin 2007). The origin of the eukaryotic cell was not due to gaining more ATP from the endosymbiotic process, since bacteria did not release ATP to the local microenvironment (Martin et al. 2001). It seems to be more likely that endosymbosis was stable for the entire evolution of the eukaryotic cell, except some negligible secondary lists of organelles in a few cell types, due to the effect of the metabolic syntrophy, where the endosymbiont (synonymously the organelle) delivered products for the host cell that are required for cell growth, such as H2 gas, in the hydrogen hypothesis (Martin and Müller 1998). The hydrogen hypothesis predicts how the first eukaryote was generated. In specific detail, the hypothesis is based on linked biochemistry of energy metabolism. The origin of eukaryotes was based on a symbiosis event between an anaerobic, strictly hydrogen dependent, strictly autotrophic archaebacterium (the host cells) and an eubacterium (the symbiont), which was able to respire, but it still generated molecular hydrogen as an end product serving as waste in an anaerobic heterotrophic metabolism. Hence, the requirement of molecular hydrogen by the host cell that has been

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generated by the symbionet was the selection principle for the common ancestor of eukaryotic cells. As a result, the more endosymbionts are present, the more substrates are available, and the greater the amount of cellular growth. For the host cells, it was an advantage of being a larger cell and managing to develop transport pathways for organics to bacterial endosymbionts from the early onset, since the selection pressures were broken down due to the fact that the adaptation to the pressure was performed by internal factors. Moreover, from the point of view of the host cell, the larger cell size, the advanced transport systems and the increased complexity of the cytoskeleton from generation to generation seem to be an advantage (Lane 2015). Ultimately, ATP became an integral factor with the emergence of an ATP/ADP translocator. The eukaryotes then had too much ATP, if any at all, which had to be quickly returned to ADP to effectively control their own mitochondrial membrane potential (Radzvilavicius and Blackstone 2015). The excess of ATP was used for infrastructure programs such as a dynamic remodeling of their cytoskeleton and elevated membrane trafficking that did not impede free flow through metabolic pathways, but rather made it easier to move, endocytose and ultimately phagocytose (Garg and Martin 2016). The process of phagocytosis must have caused the necessary de novo evolution and the extremely high expression of hundreds, or even thousands, of new genes, which is a huge energy cost that appears to go far beyond any prokaryote (Lane and Martin 2010, 2016). As soon as all these machines had improved and the advantages were there, the energy needed to sustain a phagocytic lifestyle was certainly transformed. The reason for this behavior is the inherent nature of phagocytosis on its own. While phagocytes reside in an organically rich environment, they are able to remain alive through fermentation only, they do not have to compete in rate of growth, contrary to bacteria, they simply eat the opposition. Phagocytes can therefore continue to survive through fermentation alone and lose their mitochondria, and morphologically, simple phagocytes with slightly enlarged genomes among the archetypes, such as Tritrichomonas Foetus (177 Mb) (Zubacova et al. 2008), all coming from more complex ancestors, are known. A critical difference exists between the origin of phagocytosis, which required mitochondria, supplying both energy and selective propulsion, and the maintenance of phagocytosis, which required only enough food to ferment to allow simpler phagocytes to completely eliminate their mitochondria. This distinction is strict, but has occasionally been forgotten (Booth and Doolittle 2015; Lane and Martin 2016).

References J.M. Archibald, One Plus One Equals One: Symbiosis and the Evolution of Complex Life (Oxford University Press, New York, 2014) J.M. Archibald, Endosymbiosis and eukaryotic cell evolution. Curr. Biol. 25, R911–R921 (2015) J.M. Archibald, P.J. Keeling, Recycled plastids: a ‘green movement’ in eukaryotic evolution. Trends Genet 18, 577– 584 (2002) S.M. Berch, H.B. Massicotte, L.E. Tackaberry, Republication of a translation of ‘The vegetative organs of Monotropa hypopitys L.’ published by F. Kamienski in 1882, with an update on Monotropa mycorrhizas Mycorrhiza, vol. 15, pp. 323–332 (2005) L. Bogorad, Evolution of organelles and eukaryotic. Genomes Sci. 188, 891–898 (1975) L. Bonen, W.F. Doolittle, On the prokaryotic nature of red algal chloroplasts. Proc Natl. Acad. Sci. USA 72, 2310–2314 (1975) L. Bonen, W.F. Doolittle, Partial sequences of 16S rRNA and the phylogeny of blue-green algae and chloroplasts. Nature 261, 669–673 (1976) L. Bonen, R.S. Cunningham, M.W. Gray, W.F. Doolittle, Wheat embryo mitochondrial 18S ribosomal RNA: evidence for its prokaryotic nature. Nucleic Acids Res. 4, 663–671 (1977) A. Booth, W.F. Doolittle, Eukaryogenesis, how special really? Proc. Natl. Acad. Sci. USA 112, 10278–10285 (2015) L. Brocchieri, S. Karlin, Protein length in eukaryotic and prokaryotic proteomes. Nucleic Acids Res 33, 3390–3400 (2005)

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3

Biomechanical View on the Cytoplasm (and Cytosol) of Cells

Abstract

This chapter presents the cytoplasm and the contained organelles from a biomechanical point of view. Thereby the entire cell is treated as gel with associated physical features. General physical principles are applied on cells at a mesoscopic length scale. However, on a submesoscopic scale, the mechanical properties of organelles, such as mitochondria, contribute to the overall mechanical properties of the cell. A cell may even respond to the applied mechanical stress by regulating the fusion of fission of the mitochondria. Hence, mitochondria seem to fulfill a key role in mechanotransduction processes in cells. Finally, it is discussed why mitochondria are predestinated for providing a mechanical response to external mechanical perturbations.

3.1

Biomechanical Picture of the Cytoplasm

From a biomechanical point of view, the cell can be seen as a gel (Pollack 2003). A gel is defined as a cross-linked polymer network that is swollen by the absorption of a liquid. The network maintains the liquid in its spatial position (interstitial space), while the liquid impedes the polymer network from collapsing. The overall properties of a gel are based on its equilibrium state, dynamics and kinetic properties. However, the properties of gels rely on the interaction between the polymer network and the liquid. The swelling of a gel cannot be understood by knowledge of the equilibrium volume in a solvent alone. In addition, thermal fluctuations of local swelling and shrinkage and the time dependency of volume changes due to multiple external perturbations must be taken into account. The relative position of the gel to the boundary of the phase transition affects the characteristics of the gel. The gel state depends on the temperature, osmotic pressure, composition of the solvent and the degree of swelling. The phase transition of a gel is characterized by a discontinuous volume-phase transition evoked by minute changes in temperature, composition of the solvent, pH, ionic composition or applied electric field. Since these phenomena of gels are universal, there are common physical principles underlying the phase transition of gels. These gel-like characteristics of the cell’s cytoplasm have not been the focus of engineering design, but may possible reveal features for bioengineering or biomaterials. In particular, the knowledge of cell mechanical properties is crucial for coordinated processes including differentiation, morphogenesis and maturations of cell populations in complex tissues (Costantini and Kopan 2010). © Springer Nature Switzerland AG 2020 C. T. Mierke, Cellular Mechanics and Biophysics, Biological and Medical Physics, Biomedical Engineering, https://doi.org/10.1007/978-3-030-58532-7_3

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Although systems biological approaches have attempted to understand highly complex developmental process by analyzing the regulatory networks of genes (Davidson et al. 2002; Materna and Davidson 2007, Stathopoulos and Levine 2005), there is not in all cases an advancement over reductionist approaches. The overall characterization of a cell or tissue depends on the processes of morphogenesis, differentiation and homeostasis that rely on the cell’s behavior and function, which is controlled by the environmental cues and gene expression. From a biomechanical point of view, when the cell is simply a gel, the cell functions can be understood by understanding the gel function. The knowledge of the principles underlying gel dynamics has increased greatly (Sadati et al. 2013; Pegoraro et al. 2016; Nogucci 2019; Bi et al. 2016; Oswald et al. 2017; Mierke 2018; Mongera et al. 2018). A crucial mechanism in biology is therefore the polymer-gel phase transition, which is a major structural alteration that can be caused by discrete changes of microenvironment. Phase transitions help the cell to perform work required for mechanisms, such as the migration of cells out of a cluster of cells. A polymer-gel-based foundation for cell function can be used to analyze how much this foundation explains how the cell functions and how these principles can be utilized for future engineering design. What are the Differences Between Cytoplasm and Cytosol? From the biological point of view, the cytoplasm constitutes a thick liquid, which is a filling in every cell and is surrounded by the cell membrane. The main constituents of cytoplasm are mainly of water, salts and proteins. In eukaryotic cells, all material outside the nucleus and inside the cell compromises the cytoplasm. All of the described organelles of eukaryotic cells, such as endoplasmic reticulum and mitochondria, are located within the cytoplasm. The cell nucleus is surrounded by cytoplasm and contains nucleoplasm. The cytoplasm consists of cytosol, the cell organelles, excluding the nucleus, and several insoluble substances. Besides organelles, the remaining part of the cytoplasm is referred to as the cytosol and represents the liquid part of the cytoplasm. Although there seems to be no form or structure present in the cytoplasm, it is extremely organized. More precisely, a scaffold of proteins, termed cytoskeleton, structures the cytoplasm and subsequently, the entire cell. In detail, the cytoplasm contains two basic components, which are termed the endoplasm and the ectoplasm. The location of the endoplasm is the central part of the cytoplasm, where the organelles are located too. The outmost region of the cytoplasm is the location of the ectoplasm, which represents a gel-like substance within the cell. The cytoskeleton is a network of microfilaments that is surrounded by the cytosol. Among internal organelles are mitochondria, Golgi apparatus, lysosomes and vacuoles. However, specific insoluble substances, such glycogen, starch and lipids, are detected additionally in the cytoplasm, which are termed cytoplasmic inclusions. In contrast, salts, sugars, various enzymes, amino acids and fatty acids are dissolved in the cytoplasm. What are the Functions of Cytoplasm? Commonly, the cytoplasm surrounds the internal cytoskeleton and the organelles inside the cells and thereby structures the entire cell. Moreover, the cytoplasm contains many proteins that can polymerize to a filamentous scaffold, the cytoskeleton, that helps the cell to maintain its shape and texture. These biopolymeric filaments keep the organelles at place, otherwise in the absence of filaments the organelles would localize to the basal side of the cell. Another function of the cytoskeleton is the regulation of the migration of organelles through the interior of the cell. Since the actin filaments of the cell’s ectoplasm are responsible for the migration of the entire cell, the central filaments of the cell promote the movement of organelles and even other cellular structures throughout the cell.

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Beyond the storage of multiple nutrients, the cytoplasm is the place for many important cellular functions, such as protein synthesis and anaerobic glycolysis, and activities, such as differentiation, cell division, cell growth, reproduction and responses to their microenvironment. The gel-like substance, the cytoplasm, aids the cell in transport, absorption and the processing of the required nutrients. In detail, the cytosolic enzymes facilitate the cleavage and degradation of large molecules and hence aide the organelles to further process them. An example are mitochondria that are not capable to utilize the cytoplasmatic glucose molecules. Instead, the cytosolic enzymes are required to cleave glucose into pyruvate that can then be further processed by the mitochondria. The activities of the cytoplasm include the exchange of chemicals between organelles and the cytosol. The cytoplasm therefore represents the linking element for the organelles within the cell and synchronizes mitochondrial functions with specific cellular functions. However, there are many more functions of the cytoplasm that are presented in the following chapters.

3.1.1 Cells Serve as an Example for Engineered Designs The cell-driven engineering design is still developing slowly. One reason for this is the lack of knowledge about how cells perform their tasks, which is due to the less studied physical nature of the cells. The cell represents a network of biopolymers containing basically proteins, sugars and nucleic acids, whose interaction with the solvent, such as water, interaction yields a gel-like structure. This relation is based on a classical view that has been formulated by Frey-Wyssling (1953). Since, the gel-like structure is generally accepted, the so-called gel-sol transition as a crucial mechanism is still under debate (Jones 1999; Berry et al. 2000) similarly as the other consequences of the treatment of the cell’s cytoplasm’s as a gel-like structure (Janmey et al. 2001; Hochachka 1999). The phenomena and consequence of the gel-sol transition are largely investigated by engineers, polymer and surface scientists, while their findings have not been extensively transferred to biological matter such as cells. An example of such as a transfer is the wetting of tissue phenomenon (Pérez-González et al. 2019; Beaune et al. 2017; Yan et al. 2016). Another example represents the gel-sol transition that can also be employed for cancer cells degrading the extracellular matrix enzymatically in order to migrate into and through it (Berry et al. 2000). One reason could be that practically all cell biological mechanisms are based on the idea of an aqueous solution or, more precisely, on the free diffusion of dissolved substances (solutes) in an aqueous solution. Moreover, multiple diffusional steps may be required for an action to take place. Among these steps are ions that diffuse into and out of membrane channels or membrane pumps as well as through the cytoplasm and proteins diffusing to other proteins, such as substrates diffusing in the direction of enzymes. This cascade of diffusional steps is influenced by intracellular processes that are largely independent of the gel-like nature of the cytoplasm, where the diffusion process is sufficiently slow and is hence biologically non-relevant. This issue has not been addressed by cell biology, as this discipline is not associated to the gel function of the cytoplasm. Finally, the gel-like nature of the cell’s cytoplasm has not been taken into account, while the gel-like nature of the cell’s microenvironment has recently been revisited in several reports regarding the engineering of biomaterials (Yan et al. 2016; Echalier et al. 2016, Owensa et al. 2016).

3.1.2 Cells Are Treated as Gels Gels generally consist of a framework of long-chain polymers, which are usually cross-linked to one another and solvent-containing. The cytoplasm is highly similar to gels. Cellular biopolymers, such as proteins, polysaccharides, and nucleic acids are commonly long-chained molecules, frequently cross-

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linked to one another to assemble a 3D matrix scaffold. The matrix retains the solvent (water), which is uniform—which is also maintained during the removal of the cell membrane. For instance, “skinned” muscle cells still hold water similar to classical gels. More precisely, the cytoplasm behaves therefore very similar to an ordinary gel. How the gel cell matrix retains water is of considerable relevance (Rand et al. 2000), and there are minimally two mechanistic choices. The first choice is based on osmosis. More precisely, charged surfaces attract counterions causing the influx of water. The second choice are water–molecule dipoles adsorbing onto charged surfaces, such as the cell membrane, and subsequently these dipoles assemble onto one another to create multilayers. However, the first choice does not seem to be dominant, since firstly, if the gels are in a sufficiently large-sized water bath, the gels may be depleted of counterions. As a consequence, no water can be held and the gels are then be dehydrated. But instead it has been experimentally determined that the gels are still hydrated. The secondly choice is that if the cytoplasm is placed under high-speed centrifugation, it loses ions before it loses water (Ling and Walton 1976). The second hypothesis, that charged surfaces are attracting water dipoles in a multilayered structure, was formulated a long time ago (Ling 1965). More precisely, it is suggested that the water molecules build layer by layer. However, this is still intensively discussed, but is based on important observations. Among these observations, the classic first is that polished quartz surfaces adsorb water films with 600 molecular layers thickness in a humid atmosphere (Pashley and Kitchener 1979). This also means that the water layers are built up one after the other. A second set of observations are that a force is necessary to displace solvents sandwiched between closely spaced parallel mica surfaces (Horn and Israelachvili 1981, Israelachvili and McGuiggan 1988; Israelachvili and Wennerström 1996). The overall behavior can be described by the classical Derjaguin, Landau, Vervey and Overbeek (DLVO) theory. The DLVO theory has been formulated to describe the stability of colloids and has proven to be the key factor in explaining the interactions between colloidal particles and the way they aggregate. More specifically, the DLVO theory can be utilized to rationalize forces, which act between interfaces, and to explain the deposition of particles to planar substrates. In addition, the DLVO theory can interpret the forces between planar substrates, such as in thin liquid films. First of all, the DVLO theory was initially defined for two identical interfaces representing a symmetric system that describes the aggregation of identical particles, the chemical process is known as homoaggregation (Fig. 3.1). In a later stage, this initial concept was refined to two different interfaces, which represents an asymmetric system, and hence the aggregation of diverse particles can be explained for the chemical process termed heteroaggregation. In the borderline situation of large size differences between individual particles, this is similar to particle deposition on a planar substrate (Fig. 3.1). The well-known DLVO theory is a helpful framework to predict interactions in aqueous colloidal suspensions and their rates of aggregation. More precisely, the theory is based on the assumption that the interaction forces can be estimated well by a superimposing van der Waals and double-layer forces. Within a symmetric system or for homoaggregation, van der Waals forces act attractively and double-layer forces react repulsively. However, when the system is asymmetric and heteroaggregation occurs, it becomes quite more complex. Although the van der Waals forces are still normally attractive, forces of the double-layer forces may be attractive, repulsive or both types. In detail, the charge regulation may have an effect on the overall type of forces. Furthermore, the DLVO theory can help to explain experimental situations in a proper manner. In distinct cases, the DVLO theory can predict the interaction forces and the rate constant for the aggregation rate constants in a quantitatively manner. Even at higher salt concentrations, the deviations can possibly still persist. A series of regularly spaced peaks and valley can be superimposed to an anticipated monotonic response. Moreover, the distance between the peaks equals the molecular diameter of the fluid that is sandwiched. Hence, it can be concluded that the force oscillations are based on the molecule layering between the surfaces.

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Fig. 3.1 Illustration of the DVLO theory for homoaggregation (top left) and heteroaggregation (bottom left) and an energy distance diagram (right)

The experiments by Israelachvili showed that near charged surfaces, the molecules are layered. However, they do not reveal whether the molecules are interconnected. Other later experiments reported that carbon-nanotube-tipped AFM probes that are approached to flexible monolayer surfaces display a similar molecule layering in water (Jarvis et al. 2000). Hence, these results imply that the ordering of the molecules is not simply based on the constraints of the molecule packing. In addition, the Pashley/Kitchener experiment leads to the suggestion that even multiple layers can arrange. Hence, a distinct kind of layering is diagrammed (Fig. 3.2) and collectively implied by these experiments. When the two charged polymeric surfaces are located in close proximity, the interfacial layers of water molecules can even bond the surfaces similar to a glue. This phenomenon can be observed in commonly in nature and hence is an everyday experience. As long as the slides are not wet, a separation of the two glass slides that stacked face-to-face is still possible. However, when the slides become wet, a separation is no longer easily obtained, since the sandwiched water molecules hinder the separation. More precisely, they adhere stubbornly to the glass surfaces and to each other and thereby prevent the separation. A similar principle can be found in sand: When walking in dry sand at the beach, your foot will ordinarily sink deeply into the sand, which creates a large imprint. However, when you walk in wet sand instead of dry sand, your foot will not sink deeply in it and hence the imprint is shallow. More precisely, the water adheres to the sand particles and combines them with sufficient strength to carry the full weight. When we transfer this observation to cells, the cytoplasmic matrix seems to have the appearance of a gel matrix. In both examples, the water molecules are retained, because they have high affinity for

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Fig. 3.2 Pashley/Kirchener experiment: The organization of water molecules (force dipoles) in the immediate vicinity of a charged surface is realized in several layers of water molecules

the charged and hence hydrophilic surfaces and their affinity to each another (Fig. 3.3). The gel is mainly composed of the polymer matrix and the adsorbed water, which thereby provides an explanation why cells without a plasma membrane still maintain their overall integrity. This gel-like structure contains several characteristics that provide cell functions. Ion partitioning seems to be an important issue. Why are there ion gradients between the extracellular and intracellular compartments? An obvious explanation can be maintenance of the equilibrium between the passive flow through ion channels and the active transport of ions with ion pumps. Hence, the low intracellular sodium concentration relative to the extracellular seems to be evoked by the sodium pump activities that can transport sodium ions against the concentration gradient from the interior of a cell across the plasma membrane into the exterior environment. However, when we treat the cells as a gel, an alternative explanation seems to be possible. Firstly, there seem to be a difference in the ion solubility of extracellular bulk water and intracellular layered and thereby so-called structured water and secondly, a difference in the affinity of various ions for the charged polymeric surfaces of a living cell (Ling 1992). For example, Na+ ions possess a larger hydrated diameter than K+ ions. Thus, Na+ ions are more strongly excluded from the cytoplasm than K+ ions, since the hydration layers of Na+ ions require more energy to be removed compared to hydration layers of K+ ions. Moreover, K+ ions possess a higher affinity for the negatively charged polymeric plasma membrane surfaces. Hence, the cytoplasm contains significantly more potassium than sodium ions (Pollack 2001). In the same manner, the gel treatment of the cell can help to explain the cell potential. Since the cell contains several negatively charged polymers, these polymers attract surrounding cations. The number of cations that penetrate the cell (or gel) is limited by the low solubility of the cations in structured water. The cations that manage to enter a cell must compete with the water dipoles for the fixed anionic charges of a cell. Subsequently, the negative charge of the cell’s interior is not completely balanced by cytosolic cations. The residual charge is approximately 0.3 mol/kg (Wiggins 1990). Finally, the cytoplasm has a net negative charge and hence possesses a net negative potential. In fact, depending on specific conditions, cells without a plasma membrane have cell potentials of

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Fig. 3.3 Organization of water molecules between two charged surfaces is arranged in several layers of water molecules

50 mV (Collins and Edwards 1971). In line with this result, gels constituted of negatively charged polymers possess comparable or even larger negative potentials, whereas gels that are constituted of positively charged polymers exhibit equivalent positive potentials. In conclusion, plasma membranes, ion pumps and ion channels seem to be not important for the generation of these cell potentials. Moreover, the gel paradigm of the cells can go quite far in explaining the cell’s most fundamental attributes, such as the distribution of ions and the existence of a cell potential. These two processes represent equilibrium processes and hence no energy for the maintenance is necessary.

3.1.3 Dynamics of Cells In order understand the function of cells, its dynamical nature needs to be taken into account. Since the cell is obviously not a static structure, it rather represents a machine that is designed for a variety of functions. Among these functions are a broad variety of mechanisms, which do not seem to have a clearly underlying theme. More precisely, each process seems to be driven by different mechanisms. Hence, a question can be raised whether there exists a common underlying theme that controls the tasks of the cell. At the first glance, a cell can be seen as a simple gel and treated like this. However, over time the cell can become specialized and hence differentiated. Thereby the gel structure and even the processes were made more complex. In view of such a line, the potential for a simple, general, underlying, gel-based theme should not necessarily be small. The identification of a common underlying theme is still an ongoing task in other disciplines. In (bio-)physics, the search for a unifying force has never ended when the protégés of Einstein continue. The fact that nature works sparingly and uses variations of a few simple principles to accomplish manifold actions is an attractive concept that, to my knowledge, has not yet been applied to cell functions, although the concept of simplicity is a key principle of technology.

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When the cell is a gel, it would be the next step to ask for a common principle that underlies this cell function. Hence, what are the principle that guides the cell’s gel function? In fact, gels fulfill a function. More precisely, they transform from one gel state to another gel state. This behavior has been revealed as the famous polymer-gel phase transition, which is similar to the well-known transition from ice to water, where a small alteration of the environment, such as a temperature increase, evokes a pronounced alteration in the structural arrangement of the water molecules (Tanaka et al. 1992; Hofman 1991). This dramatic alteration can lead to work. Similar as the formation of ice possesses the power to even causes the fracture of hardened concrete, the expansion of a gel expansion or its contraction can perform several types of work that can cause the separation of solute from solvent or the generation of forces. Among these highly useful phase transitions are the so-called time-release capsule, where during the gel-sol transition bioactive substances are secreted by the cell. Thereby the gel functions similar as a disposable diaper, when a condensed gel is subject to large hydration and extension to take on the load, such as a variety of man-made muscles. These behaviors are important for advanced research in the Physics of Diseases, since a small alteration of local environment can lead to large structural alterations. Similar to synthetic gels, the natural gels of the cells similarly perform such useful transitions. However, do they indeed fulfill these transitions? The question may require more adaptation to the situation of natural cell gels, since a cell is not a homogenous gel, but contains various gel-like organelles, each of which has its own purpose. Hence, it is more important to ask whether all or only a selection of these various organelles can perform the phase transition function. The answer is simple yes, and they can undergo a phase transition.

3.1.4 Cells, Gels and Motion The motion based on phase transitions can be subdivided into two main categories, such as isotropic and linear. Inside isotropic gels, the polymers are distributed in a random manner and sometimes they are even cross-linked. The water is kept largely by its affinity to the biopolymers, such as the proteins of the cell. Since the gel is largely hydrated, it may even possess in an extreme case 99.97% water (Osada and Gong 1993). When the gel is in a transitioned state, the prevailing polymer–water affinity yields to a higher polymer–polymer affinity, triggering subsequent condensation of the entire gel into a concentrated mass which ejects the solvent. Similar to isotropic gels, linear polymers show a transition from the prolonged to the shortened state. Moreover, the prolonged state represents a stable state, as it increases the polymer–water interactions to a maximal number and thereby increases the energy of the system to a minimum. Water structures itself layer by layer. In the shortened state of the gel, the affinity of the polymer to itself is greater than the affinity of the polymer to water, and consequently the polymer folds. It can fold completely or locally over a partial length. As the gel folds, polymer and water molecules both move accordingly. If a load is located at the end of the shortening filament, the load can also move. The phase transitions are inevitably cooperative: As soon as they are triggered, they are completed. The reason for this is the razor blade behavior of the transition: As soon as the polymer–polymer affinity (or the polymer–water affinity) prevails, the prevalence of the polymer increases and the transition is completed. An example is presented in Fig. 3.4. There the divalent ion, calcium, can evoke cross-links between neighboring polymer strands (Fig. 3.4). The presence of calcium ions can transform the prevailing affinity of polymer–water to polymer–polymer interactions. When part of the strand is bridged, adjacent segments of the polymer are more closely joined, increasing the tendency

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Fig. 3.4 Calcium and divalent cations can bridge the gap between two negatively charged surfaces and be arranged as a zipper-like condensation

for an additional calcium bridge. In this way, the local action increases the propensity to act in a contiguous segment and guarantees that the response is completed. Finally, these transitions tend toward completeness. Obviously, the polymer-gel phase transition can generate different types of movement. When the cell would use this principle, it could have an easy way to generate a wide variety of movements, depending on the type and configuration of each polymer. To all intents and purposes, a small shift in an environmental parameter such as pH, chemical content and temperature, can result in a cooperative, all-or-nothing reaction that could produce a massive mechanical response. Thus, phase transitions provide more than a theoretical value for cell organelles (Pollack 2001). Although these mechanisms are simpler in its nature than accepted mechanisms, there exists experimental evidence from them. Finally, the phase transition has been hypothesized to be a basic mechanism for cellular motion.

3.2

Anisotropic Mechanics and Dynamics of the Cytoplasm of Living Cells

Cell which under physiological conditions alteration that perturbs the distribution of the cytoskeleton and thereby leads to anisotropic behavior of the entire cell. There is evidence of anisotropy in cells under mechanical stress; however, the role of cytoskeletal remodeling due to alterations in the shape of cells that display mechanical anisotropy and its effects are not clearly understood. The role of cell morphology in the initiation of anisotropy in both features, the intracellular mechanics and dynamics has been addressed (Gupta et al. 2019). When the aspect ratio of the cell is altered by limiting its widths, the cytoskeletal mechanics can be determined with optical tweezers along the major and minor axes of the cell to identify the extent of mechanical anisotropy. In the next step, these active microrheological analyses can be linked with movements of intracellular structures or elements to quantify the overall intracellular force spectrum by force spectrum microscopy (FSM), out of which the grade of anisotropy in force and dynamics can be measured. As suggested, it turned out that

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unconfined cells displaying an aspect ratio of 1 are isotropic. In turn, when cells lost their symmetry, they become pronouncedly anisotropic in the cytoplasmic mechanics and the dynamic remodeling of the entire cytoplasm (Gupta et al. 2019). The cytoplasm can be described as an active complex material, in which multiple biochemical reactions take place that are crucial for the proper function of the entire cell. Biological processes including the migration of cells (Lange and Fabry 2013; Recho et al. 2015; Mak et al. 2016; Mierke et al. 2011, 2017; Fischer et al. 2017, Mierke 2019), mechanotransduction pathways (Wang et al. 1993) and the metastatic progression of cancer (Suresh 2007; Heyden and Ortiz 2016; Kim et al. 2018; Mak et al. 2017; Spill et al. 2016; Zaman 2013; Leggett et al. 2017) are associated with mechanical processes and are even crucial for the regulation of cell mechanics. Subsequently, many novel biophysical techniques have been reported that measure distinct mechanical properties of cells (Bao and Suresh 2003; Maloney et al. 2010; Hochmuth 2000; Bausch et al. 1999; Guo et al. 2013; Dao et al. 2003; Gupta and Guo 2017; Hu et al. 2017; Mierke et al. 2017). The majority of studies of the cell mechanics, such as cell stiffness or shear modulus, assume that cells under investigation are isotropic (Bao and Suresh 2003; Maloney et al. 2010; Hochmuth 2000; Bausch et al. 1999; Guo et al. 2013; Dao et al. 2003; Gupta and Guo 2017; Hu et al. 2017; Ehrlicher et al. 2015). Contrary, the cytoplasm of a cell presents a pronouncedly crowded, dynamically remodeling and hence heterogeneous material that is able to transform into a polar and anisotropic material by altering its morphology under specific, but still physiological conditions. In fact, it was suggested that the formation of structural asymmetry in the plasma membrane was the first decisive factor for the direction of cell polarity (Yeaman et al. 1999). The anisotropy in cells is crucial and seems to be responsible for mediating a various set of cellular functions, including the directional migration of cells (Hidalgo-Carcedo et al. 2011), cell differentiation (Huelsken et al. 2001), the regional membrane growth (Ziman et al. 1993; Wodarz 2000), immune system activation (Hershberg and Mayer 2000) and molecule transport over cell layers (Drubin 1991). In addition, there it has been state that the cell–extracellular matrix and cell–cell interactions facilitate the polarization of cells that pronouncedly causes a restructuring of the intracellular organization and subsequently fulfills a key function in the development of organisms (Thery et al. 2006). Understanding the directional changes in cells that affect the mechanical properties of the cytoskeleton due to structural disturbances will contribute to a broader knowledge of how cell polarity determines cellular processes. In fact, the restructuring of cytoskeletal elements, such as actin filaments, intermediate filaments and microtubules, is caused by changes in cell morphology that occur during external stimulation by specific substances or physiological processes and can therefore respond to different cellular stresses (Oakes et al. 2014; Zemel et al. 2010; Wang et al. 2002; TolicNørrelykke and Wang 2005) and subsequently manifest the anisotropic mechanical properties of the cell (Thery et al. 2006; Hu et al. 2004; Del Alamo et al. 2008). For example, endothelial cells can be probed with a continuous laminar flow shear stress that causes directional anisotropy within these cells and finally results in temporal and spatial alterations of the creep compliance of the cytoplasm due to the selected direction (Del Alamo et al. 2008). Another example are smooth muscle cells that explore mechanical strain and then adapt their cell mechanics by the structural remodeling of the cytoskeleton, leading subsequently to anisotropy in mechanical properties (Smith et al. 2003; Su et al. 2007). Finally, the mechanical anisotropy of the cell membrane can be evoked by the binding of distinct ligands toward their specific receptors (Irmscher et al. 2012). The majority of the studies analyzed the polarity in the generation of traction forces and the local mechanical alterations of the cell cortex. A systematic investigation of the directional component in the mechanical properties of the cytoplasm is still not yet fully known, but approaches have recently been made (Gupta et al. 2019).

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The structural remodeling of the cytoskeleton determines the overall cytoplasmic mechanics, whereas it can additionally impact the dynamical processes inside the cytoskeleton, such as the motor proteins, which act together with the cytoskeletal filaments (Hirokawa 1998; Vale 2003; Howard 1997; Kasza and Zallen 2011; Lau et al. 2003). When the cells function out-of-equilibrium (Gupta and Guo 2017; Guo et al. 2014; Fodor et al. 2015; Fakhri et al. 2014; Wilhelm 2008), the intracellular movement that is generated by coordinated and collective action of the motor proteins is coupled with intracellular forces and of course with mechanics. The intracellular forces facilitate essential functions of the entire cells including cell division (Sharp et al. 2000), migration and invasion of cells (Gardel et al. 2010) and contraction of the cell for cell division or movement (Murrell et al. 2015). Thus, it is necessary to improve the knowledge on how these forces can affect the polarity of cells from a biophysical point of view. The local restructuring of the cell’s cytoplasm caused cell shape alterations, and it has been analyzed in terms of intracellular mechanics and dynamics changes (Gupta et al. 2019). The microcontact printing technique can be employed to limit the cell width in one direction, whereas in the other perpendicular direction, the cells are not confined, which leads to highly polarized cells with increased aspect ratios (greater than 1). In addition, optical tweezers and microscopic particle tracking can be utilized to explore cell mechanics and dynamics within specific regions of the cytoplasm in at least two different directions, such as parallel and perpendicular to the principal axis of the cells. It has been found that the mechanical properties of the cytoplasm and the movement of tracer beads, which have been phagocytized by the cells, can significantly anisotropic due to the cell morphology. Moreover, the spectrum of the intracellular forces can be determined with FSM. As expected, it has been demonstrated that the anisotropic dynamics of the cytoplasm are guided by the alignment of fiber or actomyosin-based forces of the cytoplasm (Gupta and Guo 2017; Guo et al. 2014). When a cell remodels its actin and microtubules, its entire morphology can change and thereby also become less isotropic. This involves anisotropic features of cytoplasmic mechanics and dynamics. With microcontact printing one-directional confined cells with aspect ratios greater than 1 can be generated. The orientation of actin filaments and microtubules in these highly polarized cells is not randomly distributed, instead they are aligned in their major axis (longitudinal direction) and hence anisotropic distributed (Gupta et al. 2019). Finally, this anisotropy in the intracellular structures causes directionality-dependent mechanics and dynamics of the cell’s cytoplasm. Active microrheological measurements with optical tweezers revealed that cells, which possess an aspect ratio of 1, exhibit similar intracellular stiffness values independent of whether the longitudinal and transverse directions are analyzed that suggests isotropic cell behavior. When the aspect ratio of the cell increases, the intracellular mechanics are no longer isotropic, since the stiffness of the cell increases in both directions, the longitudinal and the transverse, but in each at different rates. In particular, the stiffness increases more rapidly in the longitudinal direction compared to the transverse direction that causes the elevation of aspect ratio of the cell (Gupta et al. 2019). An explanation for this behavior may be that the mechanical stress within the cell plane parallel to cytoskeletal filaments leads to a stretching of the filaments. In contrast, mechanical stress in the plane perpendicular to the filaments causes a deformation that results finally in the bending of the filaments. In detail, the bending modulus of these major cytoskeletal components, such as F-actin stress fibers and microtubules, was lower than the elastic modulus of the stretched fibers, and thereby the different stiffness of the two directions can be explained. Consequently, the stiffness in the longitudinal direction (major axis of the cell) correlates with the increasing aspect ratio of the elongated cells (Higuchi et al. 1995; Gittes et al. 1993; Needleman et al. 2005; Howard 2001). In addition, it needs to be figured out why there is an increase in stiffness in both directions of elongated (increased aspect ratio). Hence, the volume of the cells has been analyzed and correlated with the aspect ratio. In fact, it has turned out that there was a pronouncedly reduction of the volume

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of the cells, when the aspect ratio is increased (Gupta et al. 2019). More specifically, the relationship between an increase in cell stiffness and a decrease in cell volume is consistent with the relationship between a decrease in cell volume when the outflow of water from the cells is increased, an increase in cytoplasmic stiffness that can be explained by the concentration of intracellular components in the shrinking cells (Guo et al. 2017; Zhou et al. 2009). By modulation of the cell morphology that in turn causes the remodeling of the cytoskeleton, which then in the case that motor proteins act on cytoskeletal fibers, the intracellular movement can be altered. The motion of markers, such as 500-nm-sized tracer particles can be determined by calculating the mean squared displacement (MSD). When a cell breaks its own symmetry, the intracellular movement is anisotropic. The ball-like cells with aspect ratio of 1 can be treated as an isotropic material, since the MSD values in the longitudinal and transverse directions are very similar. When the aspect ratio of the cell is increased, the MSD is significantly increased in the longitudinal direction, whereas it is even decreased in the transverse direction, which clearly shows that the movements inside the cells switch to anisotropic movement behavior (Gupta et al. 2019). The actin cytoskeleton fulfills a key role, the intracellular fluctuations are random. However, in order to archive anisotropic dynamical movements in the cytoplasm, the actin filament network needs to be destroyed by cytochalasin D. As suggested, the MSDs in both directions, such as longitudinal and transverse cell axis, are broken down, and the dynamical remodeling is abolished (Gupta et al. 2019). Therefore, it seems to be likely that actin filament alignment in longitudinal (highly polarized) cells is a key factor in the regulation of the anisotropy within cytoskeletal dynamics. The hypothesis that the actin cytoskeleton regulates the anisotropic motion of the cytoplasm is not easy to validate, as the active forces of this structure and the scaffold of the entire cytoskeleton are coupled. In fine detail, the molecular motors that are the key producer of intracellular force are interconnected with structure of the cytoskeleton and subsequently, they function together (Schliwa and Woehlke 2003; Spudich 1994). Another point is obvious that the force direction due to the polymerization and depolymerization of the cytoskeletal components, and cytoskeletal filaments also correlate with the overall structure of the cytoskeleton ( Desai and Mitchison 1997; Mogilner and Oster 2003). Consequently, the cell symmetry seems to be disturbed by the alignment of the actin filaments and the associated biased forces dependent on this alignment, leading to anisotropic dynamics in the cells. The anisotropic MSD of cells and its associated increased aspect ratio can be investigated by measurements of the aggregate intracellular force with FSM. In fact, in the longitudinal direction the amount of intracellular force correlates with the increasing aspect ratio. The increased force that facilitates intracellular movement appears to be more pronounced than the increased resistance measured as the stiffness that increases the MSD in the longitudinal direction of the cell due to the cell’s increase in aspect ratio. However, the force in the transverse direction of the cells is not altered, when the aspect ratio is elevated. Moreover, an increase in the stiffness of the transverse direction decreases the MSD (Gupta et al. 2019). When the cell is not under structural confinement, the FSM displays that the intracellular forces in the longitudinal and transverse directions are nearly the same magnitude confirming that the cells possess isotropic dynamical properties. When the cell’s aspect ratio increases, the cells are no longer isotropic and instead, they display anisotropy or forces that rely on the direction. This finding indicates that the anisotropy in the fluctuations of intracellular forces seems to be based on alterations of the cell morphology. Another result is that the transition point of the plateau (short-term) to the apparent diffusion behavior (longterm) of the MSD takes place later in the transverse direction. An explanation for this behavior may be the decreased power that is generated by the active elements in the transverse direction (minor axis) of the cell, which has been reported with analysis of the force spectrum (Gupta et al. 2019). Worth highlighting is the recent evidence that cells work far from equilibrium (Guo et al. 2014;

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Fodor et al. 2015; Fakhri et al. 2014). For this reason, intracellular motion is characterized by both cytoplasmic mechanics and non-equilibrium forces inside the cell. Therefore, the mere monitoring of intracellular fluctuations does not supply us with an insight into the mechanical properties of the cell (Gupta and Guo 2017; Guo et al. 2014; Fodor et al. 2015; Fakhri et al. 2014); however, an exception applies to high frequencies (Gupta and Guo 2017). An example are combined experiments with active microrheology and particle tracking, which demonstrate that the increase in MSD with increasing aspect ratio of the cell is more due to increased intracellular forces than to a decrease in stiffness of the longitudinal direction of the cell (Gupta et al. 2019). Finally, these findings demonstrate systematically that the morphology of a cell seem to be a key regulatory element of the anisotropic behavior of the cytoplasm that can be seen in cytoplasmic mechanics, dynamics and forces. The anisotropy in the mechanical properties has been established through the alignment of cytoskeletal and the anisotropy in dynamical properties has been found to be dependent on anisotropic forces and a distorted cytoskeletal structural scaffold (Gupta et al. 2019). In fact, it is known that various cell types including epithelial or endothelial cells and fibroblasts exhibit aligned cytoskeletal fibers, when they are polarized and the aspect ratio is increased (Versaevel et al. 2012; Lee et al. 2016; Ishizaki et al. 2001). The perceived anisotropic mechanics, dynamics and forces are actually a result of the transformation of the cytoskeleton into a better aligned structure when cells crack their symmetry. That is why we assume that these findings should be valid for various cell types in general. In conclusion, these findings highlight the importance of the directionality-based intracellular mechanics, dynamics and forces inside a cell, when the cell exhibits a shape that is distant from isotropicity. This basic insight into how cell polarity affects the mechanics and dynamics of living mammalian cells will provide a deeper knowledge of the physical aspects of biological processes, including cell migration and invasion, cell and tissue differentiation, the intravasation and extravasation of cancer cells, in of which occurs the induction of the polarity of the cells.

3.3

Relation Between the Shape of the Nucleus and Cytoskeletal Mechanics

The connection between the cytoskeleton and the nucleus of cells is crucial for several major cell functions, such as proliferation, differentiation, extracellular induced signal transduction pathways and the formation of tissues. However, the mechanical linkages of the cytoskeleton and the nucleus are not yet clearly revealed. In order to gain more knowledge about this linkage, 2D particle scale tracking microrheology has been performed together with the cell morphological analysis. In particular, distinct human stem cell types in their adhesive state can be identified due to specific cytoplasmic mechanical features under defined cell culture conditions. It has been reported that the shape of the nucleus quantifies the mechanics in the perinuclear cytoskeleton of multiple stem cell types (Lozoya et al. 2016). The perinuclear cytoskeleton region is unique for its specific mechanical properties that cannot be found anywhere else within the entire cytoskeleton, since it contains heterogeneously distributed subregions displaying subdiffusive characteristics and that follow physical relationships, which characterize in a conserved manner multiple stem cell types (Lozoya et al. 2016). In conclusion, different stem cell types seem to be distinguishable by connection of the perinuclear mechanics to the shape of the nucleus. Indeed, the nucleus and the cytoskeleton are generally physically connected in all eukaryotic cells. These linkages enable cells to physically sense their surrounding microenvironment by employing their cytoskeleton and then transmitting the information to the nucleus, where it induces physiological reactions (Maniotis et al. 1997; Cytrynbaum et al. 2005; Driscoll et al. 2015). How these linkages induce a nuclear reaction depends on the individual cell phenotype and the cytokines it encounters in

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its specific microenvironment (Pritchard and Guilak 2006; Haudenschild et al. 2009; Godbout et al. 2013). The capacity of the cells to recognize and react to physical impulses is given throughout the entire development process. This property relies not just on the mechanical properties of the various cytoskeletal backbones, but also on their rebuilding capability under stress and on the interplay between the cytoskeleton and the nucleus (Wozniak and Chen 2009; Swift et al. 2013). Cells exploit the mechanical signals that reach this connection to tune their own phenotype during their development and adjust the physical state of the nucleus to their microenvironment (Chowdhury et al. 2010; Ingber et al. 1995). Based on these physiological interactions, the cells facilitate their organization to coordinate higher-level morphogenic mechanisms such as the collective migration of cell (Angelini et al. 2011; Tambe et al. 2011; Trepat and Fredberg 2011; Anon et al. 2012; Vedula et al. 2014) and the sorting of specific cells (Beysens et al. 2000; Cartwright et al. 2008; Foty and Steinberg 2005; Krieg et al. 2008). During the course of time, these phenomena dictate the morphogenic processes, the differentiation of stem cells and finally regulate the heterogeneity of tissues (Engler et al. 2006; Guilak et al. 2009; Calzolari et al. 2014; Grapin-Botton and Melton 2000; Moore et al. 2005; Lozoya et al. 2011). Stem cells are capable of reorganizing their cytoskeleton to control intracellular mechanics during maturation and to accommodate altered physical and biochemical conditions. In the course of the remodeling of the cytoskeleton, the cells change their cytoskeletal linkage to the nucleus to maintain their environmental awareness and simultaneously remodel their intracellular structure. In the meantime, the nucleus is able to rearrange its structure to accommodate a reshaping cytoskeleton (Heo et al. 2015). Therefore, the shape of the nucleus can be seen as an architectural signature that creates a delicate balance between the mechanical properties of the nucleus sensing the mechanical signals of the surrounding microenvironment of the cell and the cytoskeleton that is ultimately in charge of transmitting these mechanical signals through the cell, when these cells are physically connected to their immediate environment. This structural connection is based on the correlation between the shape of the nucleus and phenomenon of multipotency, which is generally found in stem cells in vitro (Lozoya et al. 2011; Heo et al. 2015; Chalut et al. 2012, 2010; Kutscheidt et al. 2014; Pagliara et al. 2014). The structural transformations lead to a path-breaking and heterogeneous physiology in the nucleus and the cytoplasm of stem cells undergoing differentiation (Daniels et al. 2010; Ekpenyong et al. 2012; Li et al. 2014; Young and Engler 2011). Afterward, these specialized characteristics can initially lead sparse pools of progenitors to arrange physiologically demanding tissues with complex multicellular assemblies and spatio-temporally regulated architectures (Foty and Steinberg 2005; Lozoya et al. 2012; Wan et al. 2008; Forgacs and Newman 2005; Forgacs et al. 1998; Foty et al. 1996). In other words, in this light, it seems clear that the cytoskeleton may have different structural characteristics between various stem cell types, but we overlook the way in which these differences are expressed in the intracellular mechanics of stem cells or how they can be manipulated to distinguish stem cell lines by physical evidence. Hence, the mechanics of the cytoplasm are explored in various human stem cells. Thereby it has been hypothesized that stem cells of different multipotency levels or altered stemness display different cytoplasmic mechanical properties. In specific detail, it was investigated whether the cytoplasmic mechanical properties can be utilized as parameters to distinguish the specific phenotypes of stem cells. Therefore, it is determined whether the cytoplasmic mechanical properties: firstly, identify the specific restructuring of the cytoskeleton, when the stem cells are stimulated with distinct soluble cytokines or the polymerization of actin is pharmacologically impaired in vitro, secondly, adjust themselves differently to the stimulated cytoskeletal transformation when the stem cell morphology was committed to a fixed geometry and thirdly, differentiate between stem cells from multiple lines (Lozoya et al. 2016). Following an investigation of the relationships

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between the cytoplasmic mechanical properties in stem cells and their cytoskeleton, we assessed whether a combination of these mechanical properties is consistent with a predictive relationship shared by stem cells in all experimental regimes, assuming that such relationships, when they exist, may indicate a structural basis present in all stem cells. In adherent stem cells, cytoplasmatic beads throughout the F-actin cytoskeleton can be localized and tracked dynamically. Thus, the cytoplasmic mechanical properties can be determined in living human stem cells with nanoscale particle tracking microrheology. More precisely, 1 lm-sized spherical beads, which are taken up by the cells through endocytosis, serve as cytoskeletal marker that can be tracked. It has so far been demonstrated that estimates of cytoplasmic mechanics in living cells with beads 1 lm or larger are consistent for microrheology regardless of whether they are trapped inside or outside endosomes (Jonas et al. 2008). Hence, the lipofection-based particle delivery to the cells was adapted for a low-titer of beads minimizing their effect of the viability and growth of the cells. After the transfection of human stem cells with beads, the beads are followed by confocal laser scanning microscopy to determine whether the cytoplasmic beads are entangled by cytoskeletal lattices or separated in vacuoles from the cytoplasm. The live microscopic analysis of actin-GFP expressing cells and the microscopic analysis of phalloidin stained fixated cells revealed that single beads colocalize with dense F-actin stress fibers along the cells’ periphery, whereas they cannot be detected within vacuoles (Fig. 3.5). Thus, after the bead endocytosis a subset of beads can be utilized to approximate the cytoskeletal microrheology in living stem cells, when they are clearly entangled with F-actin stress fibers. In order to perform parameterized intracellular rheology, a nucleus-centered elliptical coordinate system can be employed. The displacements of beads that are not covered by an endosomic membrane inside the cytoplasm displayed a pronounced directional distortion. This biased behavior has been predicted, since the cytoskeleton represents a structural scaffold composed of highly oriented filaments, which possess anisotropic mechanical properties. Moreover, it has been seen that the

Fig. 3.5 A 4.5-µm fibronectin-coated bead is connected via integrins to the F-actin cytoskeleton of mouse embryonic fibroblasts. Scale is 10 µm as indicated

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largest bead displacement directions were roughly parallel to the nuclear surface, which was found to be true for all analyzed cells (Lozoya et al. 2016). These results indicate that the anisotropic bead movement followed an orthotropic foundation, in fact one based on nuclear geometry. This interpretation contains important statistical results for the analysis of bead movement: When the interpretation is sufficient, an orthotropic model can emphasize anisotropic aberrations in the averaged space-dependent properties, which a fixed rectangular coordinate system would instead eliminate. In fact, we verified that a nucleus-centered geometric transformation better reflects the anisotropic behavior of bead displacements than rectangular coordinates. Consequently, we have constructed a curve-going and nucleus-centered elliptical coordinate system for analysis of displacements of individual beads and the estimation of the direction-dependent cytoplasmic mechanical properties. The generalized Stokes–Einstein relation can predict the behavior of the thermally driven movement of small particles within a complex fluid and the resistance of the surrounding fluid to the movement of the particles. This theory was largely taken over to approximate the cytoplasmic mechanics in living cells, when the displacement of individual particles inside the cells were monitored over time (Daniels et al. 2010; Jonas et al. 2008; Crocker and Hoffman 2006, 2007; Liu et al. 2006; Wirtz 2009; Mason and Weitz 1995; Tseng et al. 2002; Caspi et al. 2000; Fakhri et al. 2014; Weber et al. 2012; Panorchan et al. 2007; Ott et al. 1990; Mierke et al. 2008; Mierke 2011; Raupach et al. 2007; Metzner et al. 2010). In detail, this biophysical technique can be employed to examine anisotropic effects inside the cytoplasmic mechanical properties of living adhesive stem cells. In the first step, the displacements of the individual beads from nucleus-centered elliptical coordinates were broken down into their two orthotropic components. In the second step, the mean squared displacement of beads is characterized that undergo subdiffusive motion in the two orthotropic directions. Ultimately, we evaluated the subdiffuse rheological properties that correspond to the orthotropic displacements of each bead at its precise position inside the cytoplasm. In order to determine the beads included for the rheological analysis, the mathematical subdiffusive criterion has been employed that is described by the following equation of the mean squared displacement power coefficient: aðtÞ  @½logð Dr 2 ðtÞ Þ=@ ½logðtÞ

ð3:1Þ

This equation defines the slope a(t) for the mean squared displacement Dr2(t) in relation to the trace of time intervals t in logarithmic time space. In specific detail, the displacement profiles of beads, which possess power coefficients of a(t)  1 for all t, show subdiffuse movements and were traced back to their distinct cytoplasmic localization inside the cells. Their specific localization is from now on termed subdiffusive loci, which are clearly different to those locations where the beads display a power coefficient of a(t) > 1. Under this condition, the generalized Stokes–Einstein relation is mathematically invalid. While acknowledging that the cytoskeleton is highly dynamic at the molecular level, it is also clear that it must inevitably play a constant structural role at the cellular level in order to improve tissue formation. That is a crucial function that the cytoskeleton performs during early development, where the extracellular matrix is limited and cellular polarization is a critical feature of the cells to control tissue morphogenesis. The argumentation behind the extension of the generalized Stokes–Einstein relation theory to delineate intracellular mechanics in living stem cells does not support predictions of thermally driven cytoplasmic properties—rather, in general the overall non-Brownian character of cytoskeletal mechanics is excellently substantiated (Crocker and Hoffman 2007; Hofman et al. 2006; Liu et al. 2006). Instead, it is intended to detect short-lived patterns of individual bead movements with subdiffuse characteristics at specific sites inside the cytoplasm and especially at the time of measurement. Similarly, this term implicates that the type of movement emanating from each bead,

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especially when measured, may vary between measurements, test periods and in the course of time. Experience has been consistent with such requirements: Beads with subdiffuse movement at a given range of 30 s did not automatically show a subdiffuse pattern at later points in time. Therefore, we tracked all observed beads and each one and at each test interval, checked whether their motion patterns match the subdiffuse motion during data analysis in every case, and administered the data set based on cases of local subdiffuse characteristics instead of beads themselves. Hence, it can be assumed that the underlying mechanics of the cytoskeleton can be approximated in this manner. When applied to a large number of living cells, this strategy provides other benefits, such as it delivers estimates of the rheological properties of the cytoplasm of individual living cells monitored in space and over time at the physiological level. In addition, there are enough cases of subdiffuse motion to generate statistical distributions of mechanical properties that can be used to distinguish between cell populations. This is faster than searching for cytoplasmic pearls with persistent subdiffuse properties, which is improbable within living cells. Overall, the combined realization of the generalized Stokes–Einstein relational theory with nucleus-centered elliptic coordinates resulted in this: First, subdiffusive loci are predominantly situated in an area within a nuclear radius of the nuclear perimeter, which we classify as a perinuclear cytoskeleton; second, the perinuclear cytoskeleton of human stem cells maintains a constant anisotropic relationship of radial to angular shear modulus; and third, cytoplasmic rheology of living stem cells pursues power-law characteristics including frequency-invariant viscoelastic damping ranging from 0.2 Hz < ƒP < 5 Hz. Similar to the nucleus-centered elliptical coordinate system, the powerlaw rheology is also inherently accessible to non-dimensioning. That is why we have lowered the number of parameters in the analysis by extrapolating the rheology and position of each subdiffuse loci trapped in stem cells to a uniform, dimensionless projection screen with the principal following parameters: first, the nuclear elliptical shape S; second, angle to the bead from the main elliptical axis X; third, distance of the bead to the nuclear center of gravity H; fourth, the anisotropy K; and fifth, the fluidity N. Beyond that we have also evaluated the damping ratio G′′/G′ at ƒP = 1 Hz to provide a reference value for viscoelasticity. In a first experiment, the signaling of cytokines on the cytoskeletal mechanical properties in human adipose-derived mesenchymal stem cells (hASC) has been explored. Concretely, soluble cytokines are promotors of the remodeling processes of the cytoskeleton in adult stem cells, such as hASCs. In order to prove that active modulatory capacity, two different cytokines are explored on hASC cytoplasmic rheology after one hour of incubation of the cells with interleukin 1a (IL1a), which represents an inflammatory cytokine that can trigger the transport of de novo and recycled G-actin monomers of depolymerized F-actin stress fibers toward the cortex of the cell, where these actin fibers polymerize (Pritchard and Guilak 2006), and tumor growth factor b1 (TGFb1), which is an anabolic growth factor facilitating the spreading of cells, F-actin stress fiber assembly and finally the stabilization of F-actin stress fibers (Jungmann et al. 2012). Both stimulations cause alterations of the cytoplasmic rheology in hASCs compared to untreated control cells, and in all instances, such effects were associated with a decrease in angular slope X of subdiffuse locations relative to the principal axis of the nucleus, even without altering the relative distance Ʀ = H − 1 of the beads from the perimeter of the nucleus, where it is defined: H = 1. In particular, the IL1a-stimulation of the cells leads to a pronounced decrease in all Gu, Gv and G*2D shear moduli including an alteration of the nuclear shape S, enhanced fluidity N and elevated anisotropy K. The losses in the shear moduli after IL1a stimulation are slightly smaller than the losses after TGFb1 stimulation. The restructuring of the cytoskeleton after TGFb1 stimulation causes distinct structural characteristics similar to those occurring after IL1a stimulation in hASCs. However, there are differences of both treatments: In more detail, TGFb1 stimulation does not alter the nuclear shape S or the anisotropy K, which is dissimilar to IL1a stimulation that causes pronouncedly alterations is the nuclear shape and the anisotropy.

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Since there are diverse mechanical features that are stimulated by each of the two cytokines, it can be considered that the collective of non-dimensional perinuclear cytoskeleton parameters for each condition has contributed to typical mechanical markers. In fact, the stimulated hASCs cells exhibit perinuclear cytoskeleton mechanical properties that can be segregated in a good manner form untreated controls using multivariate profiling. Another second experiment deals with the restructuring of the cytoskeleton in hASCs exhibiting polarized and hence directed cell morphology. In order to analyze the correlation between cell morphology and cytoskeletal mechanics in a quantitative manner, PVA-film microphotopatterning (lPP) techniques (Doyle 2009) were employed to precisely regulate the morphology of hASCs (Lozoya et al. 2016). The hASCs were confined on a flat fibronectin-coated surface of a rectangular size of 15 lm  70 lm. To alter the structural architecture of the cell cytoskeleton, the pharmacological drug cytochalasin D impairs the polymerization of F-actin stress fibers and thereby alters the entire structure of the actin cytoskeleton of the cells (Cooper 1987; Holzinger 2009). Cytochalasin D stimulation of the cells causes pronouncedly alterations in the perinuclear cytoskeleton mechanics, since the cell’s anisotropy K is decreased, which is facilitated by an increase in Gv. Together with this mechanical result, subdiffusive loci at higher angle inclination X with lower Ʀ values were detected, which reflects a shift of subdiffuse loci positions accumulating in the proximity of the nucleus. In specific, these effects on morphologically restricted HASCs were not supported by any modification of the nuclear shape S, fluidity N, or radial stiffness Gu. Overall, hASCs treated with cytochalasin D with structured morphologies based on multivariate perinuclear cytoskeletal mechanics were poorly separated from untreated hASCs. In a third experiment, the evolution of the perinuclear cytoskeleton mechanical properties in hASCs, when in these cells a restructuring of the F-actin stress fibers is stimulated. For the purpose of mimicking the time-dependent activity of perinuclear cytoskeletal mechanics in cells under “continuous” catabolic induction, individual cytoplasmic beads were tracked in single hASCs for 30 s at regular intervals of 15 min over two hours that have been stimulated with IL1a or Cytochalasin D. As expected, both stimulations caused alterations in the cytoskeleton of hASCs involving active restructuring of the cytoplasm for up to two hours. The exchange of growth medium with treatment medium occurred 5–10 min after the single-cell analysis for nanoscale particle tracking. Both treatments affected the fluidity N, radial modulus Gu, and all morphological parameters, such as subdiffusive loci to nucleus distance Ʀ, nuclear shape S and subdiffusive loci at higher angle inclination X, and showed fast stabilization. In the 5–10 min of initial stimulation with either IL1a or cytochalasin D, evoked a pronounced increase in N (fluid-like rheology), a substantial drop in S (shape of the nucleus) and intermediate reductions in Ʀ (subdiffusive loci to nucleus distance). The other parameters, such as Gu, G*2D, Gv, K and X, were distinct between the two substances: The treatment with IL1a leads to increased anisotropy K, due to reduced Gv, while there are losses in X, whereas the treatment with Cytochalasin D leads to alterations in G*2D, K and Ʀ. While the findings after IL1a stimulation of spatially confined cells are comparable to the IL1a induction experiment, the findings for cytochalasin D differ due to the spatially confinement. Based on these different results for cytochalasin D due to cell morphological alterations, it can be asked whether also alterations in perinuclear cytoskeleton mechanical properties, as it has been seen after stimulation with cytochalasin D in unconfined hASCs. In specific, multivariate profiling of the influence of cytokines in spatially unconfined hASCs revealed pronounced effects. In order to determine whether multivariate states of perinuclear cytoskeleton mechanical properties across various types of human stem cells were addressed in a fourth experiment. In specific detail, it was investigated whether different types of stem cells with different levels of multipotency have mechanical inequalities. Hence, the mechanical properties of the perinuclear cytoskeleton and the morphologies of hASCs, human bone marrowderived hMSCs and skin fibroblast-derived human-induced pluripotent stem cells (hiPSCs) were

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investigated as a monolayer under non-differentiation culture conditions. It was demonstrated that increases in subdiffuse local anisotropy K, fluidity Ň, nuclear shape S, distance from core Ʀ and decreases in tilt angle from the nuclear major axis X were associated with distinct types of human stem cells in varying configurations. The various stem cell types did not differ in the order of magnitude of the rheological properties, such as the 2D flow consistency index G*, radial shear modulus and angular shear modulus. However, does each individual stem cell type possess specific mechanical properties? In fact, the multivariate characterization of the three human stem cells, such as hASCs, hMSCs and hiPSCs and can be performed by discriminant analysis of the perinuclear cytoskeleton mechanical properties. Finally, non-dimensional maps of perinuclear cytoskeleton lead to the identification of diverse multipotent cell types. There are two major characteristics that are obtained by the entire perinuclear cytoskeleton non-dimensional spectrum analyses: firstly, the determination of mechanical and structural features that display pronouncedly nonlinear correlations and secondly, a finite range of bead locations around the nucleus H, which becomes a fixed upper limit. These findings indicate that the non-dimensional perinuclear cytoskeleton profiles are treated as configurations of a cytoskeleton that is based on conserved mechanical features in multiple cell types. It can be hypothesized that the mechanics of the perinuclear cytoskeleton follow a conservative relation in all phenotypes of the cells, and hence, it seems to be likely that there exists a baseline state of perinuclear cytoskeleton mechanics, which is associated with the nuclear shape and the position of the subdiffusive loci inside the cell. In more detail, the baseline configuration of the perinuclear cytoskeleton can be estimated by distributing the perinuclear cytoskeleton conditions after remodeling the cytoskeleton under multiple conditions. It has turned out that the initial state of the nucleus is that its major axis is 60% longer than its perpendicular minor axis. The nucleus is connected to a perinuclear cytoskeleton that is at most two radii distant from the nuclear perimeter, has subdiffuse properties in perinuclear regions about 35° from the major axis, behaves in a highly anisotropic manner and is radially stiffer and somewhat more solid than liquid in response to stress. Empirical relationships can be found between perinuclear cytoskeleton conditions and the shape of the nucleus. The experiments with the phenotype of stem cells revealed compensatory reactions between perinuclear cytoskeletal rheology, nuclear shape and the regional heterogeneity of subdiffusive loci. A quantitative relationship between the three parameters can be achieved by fitting an empirical mixture model using recursive Bayesian inferencing. Hence, these findings lead to the following hypotheses: First, the mechanics of the perinuclear cytoskeleton mirror a conserved coherence between rheological and geometric parameters within the subdiffuse regions inside the perinuclear cytoskeleton; and second, the shape of the nucleus and the anisotropy are connected by the positioning of the subdiffusive loci. Moreover, there seems to be a strong relationship between the reference viscoelasticity G′′/G′ at ƒP = 1 Hz and the fluidity N that has been found in materials exhibiting power-law rheology. When an elliptical coordinate basis is utilized, which was mathematically prescribed by the nuclear shape to characterize the mechanics of the perinuclear cytoskeleton in individual stem cells, it demonstrates that the heterogeneous rheological phenotypes within the perinuclear cytoskeleton are inversely associate with the shape of the nucleus and that the same empirical fit can be produced in a homogeneous, non-dimensional space in all cell types analyzed. These findings are in line with the revealed connection between the contractile history of the entire cytoskeleton, the state of the chromatin and the architecture of the nucleus (Heo et al. 2015; Chalut et al. 2010). Subsequently, this leads to the hypothesis that the structural linkages between the nucleus and the cytoskeleton need to display reciprocal mechanics.

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The implication of these results is that the mechanical properties of one, such as the cytoskeleton, can be predicted by the other, such as the nucleus, or vice versa. In view of this and the dimensionless relationships between perinuclear cytoskeletal mechanics and nuclear shape that exist between multiple stem cells with various nuclei, the results emphasize a crucial aspect that we can examine: Are these mathematical interactions between mechanical or spatial cytoplasmic properties of stem cells the same unless they do not seem similar when their nuclei have different forms? In approaching this eventuality, it can be examined whether the localization and rheology of subdiffuse loci in the perinuclear cytoskeleton is predetermined by geometry of the nucleus. Through cumulative frequency analysis, the distributions of X, Ʀ and Ň, that is, the position and fluidity of subdiffuse loci in the perinuclear cytoskeleton, were well simulated by fixed lognormal distributions, whose parameters are univariate functions of the nuclear shape S. As the remaining non-dimensional parameters are forecasted by various combinations of X, Ʀ and Ň and S, and each one is a function of S itself, the nondimensional factors can be assumed by simply determining the nuclear shape. In summary, it can be said that perinuclear cytoskeleton mechanics is numerically feasible due to the shape of the nucleus. These are results with a strong impact: It indicates that anisotropy and heterogeneity in the organization of the cytoskeleton are linked mechanical features based on a specific cytoskeletal subdomain that is located around the nucleus, bound to its structure and quantifiable by the nuclear shape. These results provide a multivariable minimization approach that offers a coherent framework for the assessment of perinuclear mechanics of adherent cells. By this approximation, it is shown that the nuclear shape of stem cells resizes the relationships between orthotropic cytoplasmic mechanics in the vicinity of the nucleus. These relationships include distinct mechanical identifiers for various stem cell types, all numerically derivable by the geometry of nucleus inside the stem cells. To put it briefly, what emerges is that mechanical and morphological profiles, along with genetic and epigenetic, are strong discriminants between phenotypes of stem cells. More specifically, both hASCs and hMSCs, which each exhibit a lower rate of differentiation than hiPSCs, are more elastic and anisotropic, have more extended nuclear cores, and their subdiffusive loci are more distant from the nucleus and closer to the principal axis of the nucleus than hiPSCs. The differences in the distribution of subdiffuse loci and the structural pre-stressing of the nucleus and its environment seem to indicate that cytoplasmic mechanics can differentiate various stem cell types in perinuclear regions. Symmetries in the cytoplasm of stem cells were consistently revealed as a result of analytical methods. The key lies in receiving a single multidimensional mapping that accommodates individuality in the shape of each cell examined and simultaneously gathers heterogeneous cytoplasmic mechanics out of many cells simultaneously. To this end, we have developed an elliptical coordinate system based on the nuclear shape of each cell and taking over orthotropic mechanics. From a geometric point of view, the strategy permits the coordinate systems of all cells, after geometric normalization in terms of their individual nuclear ellipses, to merge into a nucleus standardized plane, with all perimeters of the nucleus placed one above the other along the unit radius. From a rheological point of view, the implementation of an orthotropic model permits the acquisition of cytoplasmic anisotropic mechanics that are unique for each bead in every cell due to an elliptical and curvilinear coordinate system. The following is considered for the purpose of the investigation: firstly to be characteristic of the original position of each pearl in relation to the nucleus; secondly, to be traced only to the cell under examination if the nuclear shape is recognized; and thirdly, to approximate the cytoskeletal mechanics of beads revealing, measured, a subdiffusive window of movement within the cytoplasm of stem cells, similar to that which otherwise may or may not appear for that specific cytoplasmic bead. In a broad spectrum, these results suggest that experimental designs with cell-independent coordinates, planar rheology or single-frequency measurements can be especially demanding in identifying the mechanical signs of the differentiation transmitted by cytoskeletal meshes inside stem cells.

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An elliptically based method of measuring perinuclear cytoskeletal rheology can be applied experimentally to increase the statistical power of the data to map site-specific variations in properties compared to Cartesian schemes. For instance, shifts in Cartesian x- and y-directions of beads near the nucleus tips are compensated by those in beads on the edges when the cytoplasmic mechanics is orthotropic, thereby increasing the sample size is required to detect statistically significant anisotropic effects. For the exact same beads, this is less pronounced in an elliptical system where orthotropic distortions are statistically additive—reducing the variance, increasing the statistical performance, and reducing the number of samples needed to match the statistical significance found in Cartesian coordinates. Hence, it can be said in a conclusive manner that the mechanical properties of the perinuclear regions within specific stem cells are altered through biochemical and morphological effects. Finally, the structural evidence after investigating the effects of biochemical stimulations of the remodeling of the cytoskeleton in hASCs under non-confined or confined, e.g., micropatterned, morphologies reveals that: Firstly, these cytokines remodel the localization of the subdiffusive loci within the cell that is commonly associated with alterations in the shape of the nucleus; secondly, the relocation of the subdiffusive loci within multiple cell morphologies leads to modifications in the anisotropy in cell mechanics; and thirdly, perinuclear cytoskeleton network senses the disruption of F-actin stress fibers, nevertheless create a stable mechanical profile within a few minutes, which remains intact despite the ongoing depolymerization of peripheral F-Actin. During all these alterations within the cell, perinuclear mesh networks are able to transform the shape of the nucleus and dynamically remodel the cell mechanics, which leads to the hypothesis that the perinuclear cytoskeleton works as an endogenous element in various stem cells. To this end, this non-dimensioning approach to perinuclear cytoskeletal mechanics, which quantitatively determines how cells collectively compensate for their mechanical phenotype and the architecture of the nucleus, provides a functional mapping of biophysical states in perinuclear cytoskeletal mechanics. This information provides an opportunity to consistently quantify the structural properties of the perinuclear cytoskeleton and possesses the potential to test cells based exclusively on their perinuclear cytoskeletal mechanics to discriminate between multipotential phenotypes. For the delivery of beads into cells, an endocytosis-based approach is utilized. An advantage of this approach is that it provides more cytoplasmic beads for testing than traditional bio-ballistic bead delivery. A disadvantage is that the endocytic release also yields more beads with driven or nonthermal movement, as it has been found for the shuttling of endosomes by motor proteins (Wirtz 2009; Tseng et al. 2002; Caspi et al. 2000) or in cellular locations exhibiting enormous energetic fluctuations within cells (Fakhri et al. 2014; Weber et al. 2012; Ott et al. 1990). These movements do not fulfill the generalized Stokes–Einstein relational theory, because the random thermal shifts of a spherical bead are opposed by their own resistance within a passive environment (Mason and Weitz 1995). Beads with thermally driven motions can be traced over a wide range of different time intervals to show a mean squared displacement at Dr2(t) with a logarithmic slope a(t)  ∂[log (Dr2(t))]/ ∂[log(t)]  1, where a(t) = 0 for a purely elastic medium that exhibits no movements within the solid material, a(t) = 1 for a purely viscous medium that displays free diffusion inside the Newtonian fluid, and 0 < a(t) < 1 for a viscoelastic medium that exhibits both no movements and free diffusion. In contrast, spherical sensors displaying enthalpic or persistent movement with a(t) > 1 break down the generalized Stokes–Einstein relational theory and this behavior frequently at large t. However, since the advantages of bio-ballistic techniques decrease with probe size—for example, rheological assessments are comparable between the two methods when 1 lm beads are employed (Jonas et al. 2008) and bio-ballistic techniques also produce super diffuse beads that have to be excluded according to similar a(t)-based criteria, endocytic delivery is still the preferred method (Panorchan et al. 2007).

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There is an observation that medium surrounding endocytosed beads within living cells is not passive: It rather consists of bioactive monomers, cytoskeletal fibers, hydrolyzable molecules, shuttle proteins, metallic ions and enzymes with catalytic activity in a mixture that is linked by the cytoskeleton (Jonas et al. 2008; Crocker and Hoffmann 2007; Hoffman et al. 2006; Liu et al. 2006; Wirtz 2009; Mason and Weitz 1995; Tseng et al. 2002; Caspi et al. 2000; Fakhri et al. 2014; Weber et al. 2012; Panorchan et al. 2007; Ott et al. 1990). All this is heterogeneously dispersed in the cytoplasm and leads to locally different cytoskeletal fluctuation rates from a few seconds for lamellipodia to several minutes for stress fibers. These local levels depend on the presence of specific pools and kinetics of cytoskeletal constituents and may vary over time (Baumann et al. 2012; Coue et al. 1987; Theriot 1994). For this reason, how can the generalized Stokes–Einstein relation theory be used for the shifting of beads? The cytoskeletal rearrangements oscillate between Brownian and nonthermal phases of energy transfer and differ spatially. The generalized Stokes–Einstein relation-based cytoplasmic rheology is justified if measurements originate from bead shifts that are recorded at short intervals in relation to local cytoskeletal fluctuation times, such as seconds instead of minutes in the close vicinity of stress fibers (Baumann et al. 2012; Coue et al. 1987; Theriot 1994), and correspond to a Brownian pattern. Therefore, in conducting these analyses, the following factors have been discerned: firstly, individual beads within large vesicles that display a movement dominated by the aqueous environment of each vesicle rather than cytoskeletal deflections; secondly, groups of beads that together do not present a spherical resistance pattern; and thirdly, beads bearing non-thermal movement. Therefore, brightfield or epifluorescence microscopy images need to be recorded from individual cells before measurement. To order to exclude beads in vesicles and grouped beads, they are not both traced, the latter is recognizable by the light scattering of the vesicle membranes, which can be separated from the bead contour enclosed by them. In the last step, beads with non-thermal movement (a(t) > 1) were removed from the analysis. Hence, the captured bead patterns can be used to reveal cytoplasmic rheology: The data contain cytoplasmic beads that are closely connected to Factin, monitored during a subdiffusive motion phase and spread heterogeneously within the cytoplasm, which present the reference group to those beads in subdiffusive loci. Anisotropic cytoplasmic mechanics anticipated from filamentous polymers including the cytoskeleton of living cells were previously identified based on the Cartesian coordinate systems (Tseng et al. 2002; Fakhri et al. 2014). Anisotropy within the cytoskeleton is spatially different, independent of lag time and characterized by a well-defined power law. These characteristics correspond to a contractile active gel mechanically linked to a deformable but relatively stiffer core (Hoffman et al. 2006). But here a single reference frame for cells with different geometries is provided that includes the radial distance and angle of the nucleus, the nuclear shape and the level of perinuclear anisotropy in the cytoplasm through a compositional empirical relationship, which seems to be highly conserved in different populations of stem cells (Lozoya et al. 2016). These results are consistent with a polymeric cytoskeletal network model complemented by radially interlaced filaments, an interpretation experimentally consistent with perinuclear actin cables entangled around the nucleus and connected by transmembrane actin-associated nuclear (TAN) lines (Kutscheidt et al. 2014). From a structural point of view, these physical characteristics are similar to a hydrostat (Kier and Smith 1985; Wainwright 1976), except in this case microscopically on the stem cell nucleus and with phenotypespecific mechanical conditions. The perinuclear cytoskeleton is a fiber-reinforced mechanical structure that bypasses physical cross-actions between the cytoplasm and the nucleus of human stem cells. In order to gain a deeper understanding of the relevance of the implementation of this model in the analysis of nanoscale particle tracking microscopy obtained from intracellular mechanics experiments, the elliptical mapping of perinuclear cytoskeletal mechanics can be converted into a rectangular coordinate system. With such a perspective, it is feasible to extract an empirical relationship that depicts the behavior of the “nucleocytoskeleton” as a coupled entity in which the displacement of

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perinuclear subdiffusive sites and nuclear shape changes are mutually reciprocal. This evidence-based relationship can be exploited to derive hypotheses in stem cell mechanics that can, for example, elucidate how physical responses between nucleus and perinuclear cytoskeleton develop under stress and deliver verifiable predictions in cell morphology and cytoplasmic organization. As a matter of fact, when conducting vectorial analyses of infinitesimal shear stress–strain ratios and fitting the empirical connections between subdiffuse loci localization and the shape of the nucleus, perinuclear cytoskeletal deformations tended to be mechanically linked to alterations in the shape of the nucleus. Local strains at subdiffuse sites in the perinuclear cytoskeleton of rounded cells, for example, tend to prolong the entire nucleus under stress, while an enlarged perinuclear cytoskeleton in elongated cells tends to pull inward and round the nucleus; both behaviors were immediately noticed (Driscoll et al. 2015; Heo et al. 2015). To this end, the analysis proposes that the mechanically coupled perinuclear cytoskeleton and the nucleus form a single mechanical ensemble, in which a nucleus “core” functions in combination with an enveloping and deformable perinuclear cytoskeleton, and this shows: a first, mechanical equilibrium in a pre-stressed perinuclear cytoskeleton state; second, uniform pulling through the perinuclear cytoskeleton at the nucleus along stress carrying sites; and third, stress carrying sites, which are at about 30.4 < h < 56.6° from the major axis when in equilibrium. Beyond that, subdiffuse locations in this mechanical model are suitable stress locations where the mechanical stress reaches the nucleus. With a view to the future, a practical role for the nuclear form is offered as a quantifiable marker for transient cytoplasmic mechanics in stem cells, especially as an accessible tool for dynamic estimation of perinuclear cytoskeletal properties. Cytokines have been demonstrated to modify the spatial dispersion of subdiffuse loci, usually associated with alterations in the shape of the nucleus and to compensate for the redistribution of subdiffuse loci in different cell morphologies by alterations in mechanical anisotropy. In other respects, perinuclear cytoskeletal networks are sensitive to F-actin disturbances, but within minutes they produce a stable mechanical pattern that remains intact despite prolonged peripheral F-actin degradation, indicating that the nucleus and its immediate environment were better able to withstand the structural effects of induced actin depolymerization than the outer regions of the cytoplasm where cytoskeletal integrity was severely compromised. It is therefore probable that the perinuclear cytoskeleton is an impenetrable structure that reshapes its structure to maintain physical stress, balance a structurally weakened “outer” cytoskeleton, and enclose the nucleus of the stem cell to provide mechanical protection. In this role, the perinuclear subdomain of the cell cytoskeleton actually behaves like a structural link that “buffers” the mechanical signaling on the cells on their path to the nucleus. This does not mean that this perinuclear domain is not embedded in the cytoskeleton, but that its mechanical properties vary from those of the more distant cytoskeleton. While the composition of the perinuclear region may also be relevant, it is assumed that this is due to differences in the way cytoskeletal networks are built close to and far from the nucleus. The nucleus and cytoskeleton are interactive entities that contain traceable information about the mechanical condition of cells. There are many biological processes in stem cells that affect the cytoskeleton and have strong connections between nuclear shape and cell mechanics, such as the spreading of cells (Driscoll et al. 2015; Heo et al. 2015). Not only do these variables confirm that nuclear form, cytoplasmic rheology and anisotropy are mutual: First, these variables converge to a master mapping of non-dimensional mechanics; second, stem cell types differ mainly in how they balance these properties; and third, non-dimensional cytoplasmic mechanics in stem cells can be assessed from the shape of their nucleus. It has also been noted how perinuclear cytoskeletal mechanics can rapidly achieve stability, even if peripheral cytoskeletal networks continue to become degraded. These observations point to perinuclear networks maintaining the ability to interact mechanically with the nucleus, especially during biological processes that cause large-scale structural

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alterations inside and outside cells, such as the differentiation of stem cells. Finally, it can be said that the perinuclear cytoskeleton of the stem cell in combination with the nucleus acts as a mechanically independent endogenous unit. In conclusion, it can be said that there exists a specialized function for a precisely defined perinuclear domain, the perinuclear cytoskeleton, that coordinates the linkage of the nuclear structure with the overall cell mechanical properties. In addition, evidence is presented for intracellular shape– function relationships that quantitatively determine the homeostatic mechanical equilibrium between nuclear shape and perinuclear cytoskeletal structure among a series of exogenously induced phenotypes. Finally, in human stem cells, the shape of the nucleus is adequate to reflect both the spatial dispersion and the rheological scale of abundant rheological properties within subdiffuse domains in the perinuclear cytoskeleton and to reflect different mechanical conditions within a shared parameter map which is representative of multiple cell phenotypes. This work develops a new concept for the investigation of intracellular mechanics in stem cells of different lines with different biophysical characteristics or under different metabolic regulation modes as functions of mechanical perinuclear cytoskeletal conditions, all within the probabilistic patterns as predicted by the shape of the nucleus and as such detectable solely by standard microscopy.

3.4

Cytoplasm Mechanics Are Size- and Velocity-Dependent

Many physiological processes fulfill the active transport within the cytoplasm crucial functions in living cells. However, the mechanical resistance of the intracellular compartments, which is determined by the cytoplasmic characteristics of the material, still cannot be grasped precisely, especially the dependence on size and speed. In this case, an optical tweezer is required to pull a bead into the cytoplasm and directly measure the mechanical resistance with different size a and velocity V. A method combining direct measurement and simple scaling analysis is outlined to identify different origins of size- and velocity-dependent resistance in mammalian cytoplasm of living cells (Hu et al. 2017). The cytoplasm demonstrates a size-independent viscoelasticity when the effective strain rate V/a is kept in a relatively narrow range (0.1 s−1 < V/a < 2 s−1) and a size-dependent poroelasticity when the effective strain rate mode is high (5 s−1 < V/a < 80 s−1) (Hu et al. 2017). In addition, it is shown that the cytoplasmic modulus is positively correlated with merely V/a in the viscoelastic regime, but also rises with the bead size at constant V/a in the poroelastic mode. Based on these measurements, a complete state pattern of the mammalian living cytoplasm is generated, demonstrating that the cytoplasm undergoes a change from a viscous liquid to an elastic solid, and from a compressible material to an incompressible material, with rising values of the two-dimensional parameters. These state diagrams are valuable for gaining an insight into the underlying mechanical properties of the cytoplasm in a wide range of cellular functions over a broad spectrum of velocity and size scales. The cytoplasm of living mammalian cells presents an overcrowded but dynamic surrounding (Alberts et al. 2008). There exist ongoing intracellular motions that are crucial for cell physiology, such as vesicle transport and transport of other organelles. While biological motors and other enzymatic reactions drive these activities, the mechanical properties of the cytoplasm are decisive in determining the mechanical resistance encountered by cell compartments. In fact, both the active propulsive force and a suitable mechanical surrounding are decisive for the design of the living cell machinery. But while the force produced by molecular motors both individually and collectively has been extensively explored (Hendricks et al. 2012; Guo et al. 2014), the mechanics of the cytoplasmic surroundings are still not yet clearly revealed. In addition, the influence of object size and velocity on

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the mechanical resistance that active forces must withstand in order to permit transport is still uncertain. The characterization is essential for gaining an insight into the physical landscape and various crucial dynamic functions of living cells. The cytoplasm possesses a cytoskeletal network and multiple proteins, various organelles and vesicles. Such complex microstructured materials are supposed to exhibit time-dependent or frequency-dependent characteristics (Larson 1999). Consequently, it has been detected in various biophysical approaches that the mechanics of living cells display a pronounced frequency dependency (Hoffman and Crocker 2009; Zhu et al. 2000) and the cells’ reaction show a power-law rheology behavior in a broad range of frequencies (Guo et al. 2014; Fabry et al. 2001; Hoffman et al. 2006). Based on these findings, a common agreement of all these biophysical approaches is that cells can be seen as viscoelastic materials (Janmey et al. 2009; Bausch et al. 1999; Guck et al. 2001; Lu et al. 2006; Nawaz et al. 2012; Mierke et al. 2008) and additionally, the revealed mechanical properties rely on the time period over which the deformation lasted throughout the measurement. An essential characteristic of a viscoelastic medium is the fact that its mechanical property differs according to the length scale of the measurement (Hu and Suo 2012). An interesting fact has recently been shown that living cells are able to react similar to a poroelastic gel within a short time frame (Moeendarbary et al. 2013; Rosenbluth et al. 2008; Charras et al. 2005). Moreover, the stress relaxation of cells can be completely controlled by cytosol migration through cytoskeletal grids. In terms of poroelasticity, the measured mechanical property is highly influenced by the probe’s size, as it requires more time for the cytosol to move over a greater distance, thus reducing stress relaxation. The size-dependent poroelastic behavior stands in direct contrast to viscoelasticity, whose relaxation is a material property and is independent of the size of the probe, adjusted by the time-dependent reaction of the macromolecular and supramolecular components of the materials. Even more important is the fact that most previous efforts to examine cell mechanical sensor cells from the outside, e.g., with the help of atomic force microscopy or an optical stretcher, and thus the measurement rather depends on the stiff actin-rich cell cortex than on the far softer cytoplasm (Nawaz et al. 2012; Moeendarbary et al. 2013; Guo et al. 2013). It remains uncertain whether viscoelasticity or poroelasticity adequately reflects the rate-dependent resistance of the cytoplasm of living mammalian cells or whether both are necessary. Therefore, optical tweezers are employed to pull a plastic bead inside the cytoplasm of a living mammalian cell and directly detect the force (designated by F) and displacement (designated by x) that reflect the cytoplasmic mechanical behavior. Taking into account viscoelasticity and poroelasticity, two independent dimensional parameters emerge from the experiments: V/a and Va, where V and a represent the velocity and diameter of the probe bead. With these two reference values and a combination of experimental measurement and the scaling analysis technique, various origins of cytoplasmic resistance can be identified, varying from viscous, viscoelastic, poroelastic to purely elastic. In a final step, various cytoplasmic mechanical modes are categorized in an overall state diagram that visualizes the diverse origins of the mechanical resistance of a multitude of physiological pathways in cells of dissimilar characteristic size and velocity. To elucidate the mechanical opposition that intracellular specimens encounter in a living mammalian cytoplasm, intracellular polystyrene beads are delivered into living normal rat kidney epithelial cells by endocytosis. The sizes of these beads are from 0.5 µm to 1.5 µm, and they are randomly dispersed within the cells and their size is pronouncedly larger than the typical mesh size of the cytoskeleton, which is about 50 nm (Luby-Phelps 2000). These beads are capable of examining the cytoplasm as a continuous fluid. An optical tweezer is then employed to catch and pull a single bead unidirectionally at a constant speed to the cell boundary. To prevent any interference with the mechanically separated cell cortex and nucleus, beads larger than 1.5 µm distant from the cell boundary and both from the thin lamellar region and from the nucleus are preferred. The beads are

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propelled unidirectionally with constant velocity. Therefore, the intracellular resistance force on the bead in the direction of movement equals the traction force measured. Finally, a force versus displacement curve of the cytoplasm can be drawn. In order to compare measurements with varying bead sizes, the force as F/S and the displacement as x/a need to be normalized, where S represents the cross-sectional area and a represents bead the diameter, and subsequently, a normalized force– displacement curve can be drawn. Understanding the mechanical resistance in the cytoplasm, it is estimated that the resistance of the bead is primarily the result of the restoring force of the deformed cytoskeleton and the inhomogeneously distributed nuclear pressure within the cytoplasm. The deformation of the cytoskeleton microstructure itself is typically viscoelastic, resulting from the rearrangement of the cytoskeleton microstructure and the breaking of protein bonds (Kumar et al. 2006). The viscoelasticity kinetics are characterized by intrinsic relaxation time scales or intrinsic time spectra (Balland et al. 2006; Wang et al. 2014), which are unaffected by stress conditions such as bead diameter and velocity. Cytoplasm local distortion leads to inhomogeneous pressure distribution of pores within the porous structure generated by the cytoskeleton as a result of poroelasticity (Moeendarbary et al. 2013, Detournay and Cheng 2014). In order to achieve a rehomogenization of the pore pressure dispersion, the intracellular liquid moves over representative distances according to the bead size, with typical poroelastic relaxation time a2/D. D represents the effective poroelastic diffusivity of the cytosol that scales as D  Ej (Detournay and Cheng 2014), in which E is the equilibrium elastic modulus of the cytoplasm under equilibrium conditions and j is the permeability that is defined as the cytosol flux through cytoskeletal scaffold under a uniform gradient of the pressure. In the analysis of the mechanical strength of the cytoplasm, however, viscoelasticity and poroelasticity are taken into account. The viscoelastic relaxation times and the effective poroelastic diffusivity can serve as parameters defining normal rat kidney epithelial cells. Normally, only one time scale is assigned to the loading condition, namely a/V. On the foundation of a simple scaling calculation, the ratio between the resistance force and the bead displacement, F = f (E, ti, D, m, a, x, V), may be converted into a dimensionless form, F/ES = f (x/a, Va/D, Vti/a, m), wherein S represents the cross-sectional area of the bead, m constitutes the equilibrium of Poisson’s ratio of the cytoplasm and ti comprises a series of typical viscoelastic time intervals. For a specific cell type, E, D, ti and m represent all constants. Vti/a (i = 1, 2, 3…) have no dimensions here, and the ratio of the typical viscoelastic time intervals to the experimental time interval is designated as Deborah number (Reiner 1964). Va/D denotes the ratio of the typical poroelastic time interval to the experimental time interval. If the predicted rate-dependent mechanical response found to be due only to viscoelasticity and a constant V/a is sustained, the measured relationships between F/S and x/a break down to a single curve; if the observed rate-dependent response found to be due only to poroelasticity and a constant Va is sustained, the measured relationships should break down to a single curve. In order to validate the ability of the experimental method to discern between viscoelastic and poroelastic properties, unidirectional particle stretching is carried out in classical poroelastic and viscoelastic materials with established material properties. To measure the poroelastic behavior, polyacrylamide (PA) (3% acrylamide and 0.05% bisacrylamide cross-linker) is measured, which represents a hydrogel with covalently cross-linked meshes exhibiting linear elastic behavior over a wide frequency range (Lourenço et al. 2016). Nevertheless, if the gel is exposed to a high strain rate deformation, the fluid flow within the gel network influences the material resistivity against the applied strain, and the velocity dependence is well captured by the linear poroelasticity theory (Hu et al. 2010). Beads are pre-mixed in two distinct sizes (0.5 µm and 1 µm diameter), and unidirectional stretching is performed at comparatively high velocities (5, 10, 20 and 40 µm/s). When Va is a constant, the measured normalized force–displacement curves appear to be close to each other. In addition, higher values of Va at the same strain yield higher stresses which correspond to the

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characteristics of poroelastic material. In accordance with the analytical solution of the inclusion problem of a spherical rigid inclusion in an elastic environment (Selvadurai 2016), the modulus of elasticity of the medium E can be calculated as E = [(5–6m)(1 + m)/24(1 − m)]EA, where m gives the Poisson’s ratio and EA expresses the apparent modulus, which is measured as the average slope of the normalized force–displacement curve of 0–10% normalized deformation. At a Poisson’s ratio of 0.457 (Takigawa et al. 1996), the computed elastic modulus of the PA gel is approximately 80 Pa, in accordance with the bulk rheology measurements performed on the same gel. Ionic cross-linked alginate gel made from 5 mg/ml alginate solution cross-linked with 3 mM calcium sulfate is employed to check whether stress–strain curves with the same V/a collapse in the viscoelastic regime. In fact, the viscoelastic properties of alginate gel are well defined for a wide frequency range equivalent to low strain rates (Mitchell and Blanshard 1976). The beads are pulled at relatively low speeds (0.4, 0.8, 1, 2 and 4 µm/s) where the viscoelasticity prevails over the stress–strain response. In fact, assuming constant U/a, the measured normalized force–displacement curves are near each other. With the same bead size, a higher pulling velocity leads to a higher resistance force, which also corresponds to the reaction of most viscoelastic materials. At high elongation rates, stresses in the material leave no time to relax. These results show that the method can actually discriminate between poroelasticity and viscoelasticity within a material. Beyond that, finite element simulations were also carried out to check the scaling analysis. For the simulation, a rigid bead in an elastic mass that is far wider than the bead is embedded. A constant speed on the bead is imposed. Then, the resistant force on the bead is determined. During the simulation, the elastic material is characterized either as a viscoelastic material with a Prony series (Park and Schapery 1999) or as a linear poroelastic material. Unsurprisingly, the numerical simulations once again validate the scaling analysis, and hence, this method has been employed to normal rat kidney cells. In precise detail, the normalized force–displacement curves at different loading speeds and with three different bead sizes, such as 0.5, 1, and 1.5 µm, were determined. In fact, the mechanical response of the cytoplasm strongly relies on the 2D numbers, such as V/a and Va, in two effective strain rate regimes. At low effective strain rates (0.1 s−1 < V/a < 2 s−1), the cytoplasm behaves viscoelastic, since the normalized force–displacement curves lie close together during measurements with the same V/a, such as 1, 0.5 and 0.2 s−1. If we compare measurements within this regime with the same Va, such as 0.5 µm2/s, they differ considerably from each other. By similar analyses, we observe that the cytoplasm looks poroelastic under high effective strain rate (5 s−1 < V/ a < 80 s−1), since the normalized force–displacement curves for measurements with the same Va, such as 20, 15 and 10 µm2/s are of the same nature. Within this regime, when measurements with the same V/a, such as 20 s−1 are compared, the curves can be discriminated from one another. In addition, if the effective strain rate continues to be decreased (V/a < 0.1 s−1), the resistance stays approximately constant as the displacement increases. This is a typical characteristic of a viscous fluid having an effective viscosity, which is approximated as 12 Pa s, which is similar to previous studies (Bausch et al. 1999; Berret 2016). Similar results can be obtained with HeLa cells. Similarly, it is no longer valid to consider the cytoplasm as a continuum and to use the above scaling analysis to investigate the mechanical interaction between the bead and the cytoplasm when the probe size becomes comparable or even smaller than the mesh size of the cytoskeleton mesh. However, most of the usual cytoplasmic organelles, such as vesicles and mitochondria, have a size considerably larger than the typical cytoplasmic mesh size and are indeed similar to the beads used in this case. For this reason, these measurements offer direct insights into the knowledge of the mechanical resistance that conventional organelles undergo in living cells. The cytoplasm of mammalian cells consists of a porous structure consisting of cytoskeletal structures and other proteins with cytosol filling (Moeendarbary et al. 2013). The results indicate that the cytoplasm of living mammals displays viscous fluid behavior, viscoelasticity or poroelasticity

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with varying combinations of bead size and velocity. At sufficiently high effective strain rates, the characteristic time for movement over a bead diameter is similar to the time it takes for cytosol to migrate around the bead; this results in the rate dependence seen to be caused by poroelasticity. However, at low effective stretching rates, the associated slow cytosolic movement is coupled with an essentially immediate rehomogenization of pore pressure; consequently, any remaining ratedependent resistance to bead movement is attributable to the viscoelastic response of the cytoskeletal system. In extremely low bead velocities, the cytoplasm also behaves like a viscous fluid, since the cytoskeleton has sufficient time to depolymerize and transform over very long periods of time, allowing the fully relaxed cytoplasm to experience a viscous flow during loading. In the experiments, the poroelastic relaxation time of the cytoplasm is approximately 0.02–0.1 s, assumed from the range of effective strain rates where poroelasticity is monitored; it is much less than the apparent viscoelastic relaxation time, t = 0.79 ± 0.13 s, recorded in the normal rat kidney epithelial cytoplasm. t is achieved by one-time exponential adaptation of the relaxation curve obtained from the cytoplasm. While the cytoplasm may exhibit a range of relaxation times (Balland et al. 2006), the exponential fit with a characteristic single time is somewhat in agreement with the experimental data indicating that, although there is a range of relaxation times, there is a dominant region in the time scale sharing the same order of magnitude with the matched apparent relaxation time t. During this apparent relaxation time, the viscoelastic response within the cytoplasm relaxes pronouncedly. For simplification, the apparent relaxation time t is used in the following, instead of the time spectrum ti (i = 1, 2, 3…), when the cytoplasmic viscoelasticity is addressed. The poroelastic diffusion coefficient of the cytosol inside the cells is estimated from the experimental timescale, T (=a/V), where the poroelastic regime can be detected with D = a2/T 50 µm2/s, which is in line with reported measurements in mammalian cells (Moeendarbary et al. 2013). These measurements show that cytoplasmic mechanics can possess various origins at diverse timescales: Viscoelastic and viscous behavior may be present upon relaxation, and the restructuring of the cytoplasmic scaffold can dominate cytoplasmic mechanics at larger timescales, while poroelasticity is based on fluid–solid, such as cytosol–cytoskeleton, frictional interactions and the dissipation of energy prevails at relatively short timescales. In order to reveal the impact of the cytoskeletal scaffold to the mechanical phenotype of the cytoplasm, 5 lg/mL cytochalasin D can be added to living cells to depolymerize F-actin structures, which represents a key element of the cytoskeleton, and thereafter unidirectional stretching is applied. In fact, a clear decrease in the characteristic viscoelastic timescale t can be detected and a pronounced increase of the effective poroelastic diffusivity D of the cytoplasm can be seen, which then reduces the poroelastic relaxation time. The poroelastic diffusion coefficient, representing the effective diffusion of the fluid within a porous environment due to internal pressure gradients, distinguishes itself from the usual diffusion coefficient, which takes into account the collective movement of the solutes in response to concentration gradients in the solvent environment. The depolymerization of F-actin increases the mesh size and porosity of the cytoskeleton in the cytoplasm and thus enhances the poroelastic diffusivity. The decrease in the viscoelastic time scale is probably attributable to the loss of friction between F-actin structures and other cytoplasmic components (Ferry 1980) or various actin-associated interactions. The disruption of the F-actin network thus accelerates both cytoplasmic viscoelastic relaxation and cytosolic diffusion during cell deformation. Consequently, the critical effective strain rate, V/a, which triggers poroelastic activity, rises. In addition, the expansion rate of the transition from an apparently viscous fluid behavior to a viscoelastic material also rises. The biological activity of molecular motors which seems to impact pronouncedly affects the mechanical properties of reconstituted cytoskeletal scaffolds (Humphrey et al. 2002, Koenderink et al. 2009) and elevates the cytoplasmic fluidity inside bacteria (Parry et al. 2014). To reveal how such the biological activity impacts the mechanics of the cytoplasm of mammalian cells, force-relaxation measurements in HeLa cells were performed in the presence or absence of with

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myosin II motor inhibitors, such as blebbistatin or depletion of ATP. As expected, the characteristic relaxation time in the cytoplasm significantly is elevated in normal HeLa cells after blebbistatin treatment cells and even more after ATP depletion. The inhibition of myosin by blebbistatin and the depletion of ATP depletion decrease the remodeling of the cytoskeleton, which hence elevates the specific viscoelastic relaxation time t. This finding is in line with the relaxation time of reconstituted cytoskeletal scaffolds and of the cytoplasm in bacteria (Humphrey et al. 2002; Parry et al. 2014). This particular elevation in t leads to a reduction in the transition of the effective strain rate V/a of a viscous fluid toward a viscoelastic gel. These data show that the cytoskeleton alters together with various active components of the cells the mechanical reaction of the cytoplasm through adaption of the cytoplasmic mechanical factors including the viscoelastic relaxation time and the poroelastic diffusivity. To analyze the mechanical resistance in a quantitative manner, which is sensed by a particle that moves through the cytoplasm, the rate-dependent apparent modulus of the cytoplasm can be determined from the slope of the stress–strain curve. In the next step, the rate-dependent modulus can be divided into the storage modulus and the loss modulus that are derived from specific rheological models and the Boltzmann’s superposition principle (Koenderink et al. 2009). The storage modulus and the loss modulus are frequently employed to characterize soft materials and complex fluids (Guo et al. 2014, Shuck and Advani 1972); however, the apparent modulus can be used to determine the cytoplasmic mechanics, as it is highly important for the rate- and size-dependent resistance force that a particle undergoes when moving in the cytoplasm. In fact, it has been found that all measured normalized force–displacement curves of the cytoplasm are commonly linear, if the normalized displacement is small, which means that the displacement is smaller than the bead radius. Under these conditions, an apparent elastic modulus, EA, can be determined from the average slope from 0 to 10% strain. The apparent elastic modulus seems to rely on the size of the probe and speed of the loading. To discover whether EA depends on the probe size, the 3D plot can be projected onto 2D axes, such as EA versus S. Within the viscoelastic regime, which means, when the effective strain rate (0.1 s−1 < V/a < 2 s−1) is small, EA is constant independent of the distinct probe size. When the effective strain rate (V/a > 5 s−1) is relatively large, EA depends strongly on the probe size, as it increases with the probe size, which means that the poroelasticity begins to affect the measurement of the apparent modulus and thereby it will become increasingly difficult to achieve a relaxation of the differential pore pressure as the probe size rises. Various origins of cytoplasmic elasticity can be further demonstrated by recording the apparent cytoplasmic modulus in dependence of V/a. The apparent modulus of all measurements at low effective strain rate regardless of bead size precisely matches the same pattern; EA in this regime is entirely defined by V/ a, which verifies that the resistance is primarily attributable to viscoelasticity. In this regime, the measured dependence of EA on the effective strain rate is also in accordance with the previously monitored power-law rheology with a rate of 0.2% (Guo et al. 2014; Fabry et al. 2001). But if the effective strain rate is high, there is no evident correlation between the apparent modulus and the number V/a. Instead, if Va is constant, the same module is retained; EA stays the same although the value of V/a varies several orders of magnitude. The rate dependence of the resistance in this regime is mainly determined by the poroelasticity in the cytoplasm. The transition time between these two systems is V/a = 3 s−1; both viscoelasticity and poroelasticity account for this transition. The unequal dependence of the apparent modulus on V/a and Va according to various regimes strengthens the idea that the viscoelasticity prevails over the size-independent (at a fixed effective strain rate, V/a) mechanical behavior at low effective strain rate and predominates if the experimental time scale a/V is similar to the viscoelastic time scale t, that is Vt/a  1. In contrast, the poroelasticity prevails the size-dependent at a fixed effective strain rate, V/a, mechanical behavior at a high effective strain rate if the experimental time frame is similar to the poroelastic time frame a2/D, that is Va/D  1.

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Fig. 3.6 State diagram of living mammalian cytoplasm as a function of the two dimensionless numbers Vt/a and Va/D

In the end, the various cytoplasmic mechanical characteristics within a state diagram can be revealed as a function of the two dimensionless parameters, Vt/a and Va/D (Fig. 3.6). There are eight different types of mechanical cytoplasm responses operating at various load conditions, each corresponding to one of the eight sections in the state diagram. At Vt/a < < 1 in Section I, the cytoplasm acts as a viscous fluid (Fig. 3.6). At Vt/a  1 (Sections II–IV), the viscoelasticity determines the rate-dependent mechanics of cells. At Vt/a > > 1 (Sections V–VII), the cytoplasm acts similar as an unrelaxed elastic material; if the experimental time frame is far less than the viscoelastic relaxation time, the cytoskeleton is not able to rearrange, displace or break, and thus no viscoelastic dissipation is possible. Similarly, the cytoplasm at Va/D < < 1 (Sections IV and VII) is extremely compressible, because if the experimental time frame is considerably extended than the poroelastic relaxation time, the infiltrated fluid moves freely around in cells, equivalent to significant volumetric alterations. In this case, the apparent bulk modulus corresponds to the bulk modulus of the solid part in the cytoplasm, considered to be extremely compressible. The poroelasticity adds to the size-dependent mechanical properties of the cytoplasm in the same way as Va/D  1 (Sections III and VI). At Va/D > > 1 (Sections II and V), the experimental requirement demands a high loading size and fast loading speed, impossible to reach with optical tweezers within the cytoplasm. However, based on the experimental results gained in other areas of the state diagram and the theoretical analysis of poroelasticity, the time frame of the loading seems to be too short compared to the poroelastic relaxation time in these two areas and thus the intracellular fluid is confined in the porous cytoskeleton. Under this condition, there are no changes in volumetric strength and can therefore be described as incompressible, with a bulk modulus substantially greater than its shear modulus. An interesting fact is that the cytoplasm exhibits a purely elastic behavior even if the dimensionless parameter V/a is extremely large and Va is either extremely large (Section V) or extremely small (Section VII). In these two cases, a subsequent alteration of the bead diameter and velocity fails to modify the resulting normalized force–displacement curve, pointing to a size and rate-independent elastic behavior. At the same time, the probe in section VIII is identical or smaller than the characteristic mesh size of the cytoskeleton of approximately 50 nm (Luby-Phelps 2000), and consequently, a continuum mechanics model cannot be used. This state diagram visualizes various features and multiple origins of cytoplasmic mechanics due to different characteristic sizes and speeds.

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Based on this diagram, the origin of mechanical resistance can be figured out that a wide range of physiological functions and cellular processes evoke inside the cytoplasm of mammalians. Multiple organelles exist in the cytoplasm that continuously move through it; hence, the mechanical characteristic of this resistance, which one after another commonly senses, relies on their individual sizes and velocities. Using the common size of an organelle and the typical velocity, the precise values of two dimensionless numbers for the motion of the cell nucleus, the mitochondria, the lysosome and the myocyte contraction can be determined. These values specify the distinct region that each process usually occupies within the state diagram. Based on this result, the fundamental roots of the mechanical resistance of different cellular processes across a broad range of velocity and size scales emerge. The combined analysis of unidirectional force–displacement in the cytoplasm of living mammalian cells can be performed with optical tweezers and a simple scaling analysis. Hence, a technique is presented that can discriminate between various origins of cytoplasmic mechanical resistance due to the intracellular transport. Thereby it can be revealed that mechanical resistance of the cytoplasm can be based on either viscosity, viscoelasticity, poroelasticity or pure elasticity, which relies on the velocity of object and its size. In summary, the mechanical resilience and the apparent cytoplasmic modulus rely both on the velocity and size of the probe and, even bolder, on different modes, while various mechanisms prevail over the apparent cytoplasmic mechanics. Beyond that, in a state diagram with two dimensionless variables, two types of cytoplasmic mechanical behavior are summarized; this state diagram depicts that the living cytoplasm increases from a viscous fluid to an elastic solid as a dimensionless parameter, Vt/a, and from a compressible material to an incompressible material as another dimensionless parameter, Va/D. Not only the cytoskeleton but also bioactive cell motors are able to control the viscoelastic relaxation time t as well as the poroelastic diffusivity D directly and thus influence the switch between distinct mechanical states. The cytoplasmic state diagram delivers a glimpse into the underlying mechanical nature of a wide range of intracellular transport and other cellular events over a wide variety of velocities and sizes. Finally, an extension of this state diagram to evaluate the mechanical properties of other soft materials of viscoelastic and poroelastic character can be made.

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Focal Adhesion Proteins Regulate Cell–Matrix and Cell–Cell Adhesion and Act as Force Sensors

Abstract

This chapter presents focal adhesions and the proteins they contain by selecting the most appropriate focal adhesion protein, vinculin, as a representative example. The role of a key focal adhesion protein vinculin is presented for the processes of cell–matrix adhesion and cell–cell adhesion in precise detail. Mechanotransduction signaling is also included and the role of intracellular force sensors is highlighted. Since force sensors can act as mechanical markers, their function is presented. There are different force sensors described, and an alternative myosin-independent force sensor, the lamellipodium, is presented. Finally, it is discussed why a cell needs to mechanically sense their environment and what impact it has on the cell itself.

4.1

Introduction to Cell–Matrix Adhesion

The cell adhesion represents a fundamental feature for the survival and function of various cell types and facilitates the function of basic cellular processes such as cell division, cell survival and cell migration. The dynamical interaction of cells with their surrounding microenvironment is facilitated by cell–matrix adhesions that drives many basic cellular processes including the recruitment of immune cells, wound healing after tissue injury and the malignant progression of cancer. The focus of current biophysical research is on the investigation of focal adhesions with newly developed imaging techniques that have shed light on the precise analysis of the molecular composition and dynamics of the focal adhesions. Several specific types of cell adhesions are formed by adhesive cells upon the contact with the extracellular matrix environment. A classification of these structures can be performed due to various factors including their protein composition, the lifespan of the adhesion and the proteolytic activity of the assembly.

4.1.1 The Major Adhesion Types The size of the adhesive structures in cells can vary considerably within a single cell and on different time scales (Izzard and Lochner 1980). Subsequently, these adhesive structures can be defined by two different types based on their composition of proteins, the size of these adhesive structures, their lifespan and their ability to degrade proteolytically the extracellular matrix environment (Puklin© Springer Nature Switzerland AG 2020 C. T. Mierke, Cellular Mechanics and Biophysics, Biological and Medical Physics, Biomedical Engineering, https://doi.org/10.1007/978-3-030-58532-7_4

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Faucher and Sheetz 2009; Zaidel-Bar et al. 2004, 2007). Among different types of adhesive structures, the family of integrins plays a key role for the initiation and stabilization of them. The binding of integrins to the extracellular matrix microenvironment is driven by the localization of talin on the cytoplasmic side of the cell membrane to the cytoplasmic tail of the integrins. The binding of talin to integrins initiates the further recruitment of other focal adhesion proteins, such as vinculin, focal adhesion kinase (FAK) and paxillin. Each of these plays a decisive role in the signal transduction pathways and in the entire lifetime of the structure.

4.1.2 Focal Point Contacts The focal point contacts (synonymously termed dot-like contacts or simply point contacts) represent the shortest-lived adhesive structures of the cell. They have a relatively small size of 0.5–1.0 µm (Bershadsky et al. 1985; Tawil et al. 1993) and are located at the edges of lamellipodia of adhesive cells. Moreover, these structures are also described as focal complexes (Nobes and Hall 1995). Focal point contacts possess a small size, and they are typically directly located after the leading edge of a cell that is just spreading or migrating. Moreover, these focal point contacts can be assembled and disassembled at relatively short time scales in the order of seconds or minutes. Therefore, they are supposed to have “scanned” the local extracellular matrix before either decomposing themselves or converting to more stable structures such as focal adhesions.

4.1.3 Focal Adhesions The size of focal adhesions is much larger than that of focal contacts (synonymously termed focal contacts, which are in some studies referred to smaller sized structures). These elongated streak-like structures have a length of 3–10 lm and are connected to actin filament and myosin filament bundles that are termed stress fibers (Heath and Dunn 1978; Rottner et al. 1999; Zamir et al. 2000). In addition, focal adhesions possess a higher stability than focal point contacts and are therefore available in the order of 10 min. Focal adhesions encompass a wide variety of proteins to maintain the stability of the cells and generate and exert traction forces toward their surrounding extracellular matrix environment from the cell and vice versa.

4.1.4 Fibrillar Adhesions Even larger and more stable focal adhesions can be found that are termed fibrillar adhesions. These fibrillar adhesions can be enriched with tensin (Zamir et al. 1999; Zamir et al. 2000; Katz et al. 2000) that is engaged with the fibrillogenesis of the extracellular matrix protein fibronectin (Pankov et al. 2000). These long, stable structures, which are located aligned to bundles of fibronectin in vivo, fibrillar adhesions contain besides tensin, highly enriched in a5b1 integrins (Green and Yamada 2007). At fibrillar adhesions, extracellular matrix can be repositioned and the fibrillogenesis of fibronectin can be induced. Two other adhesion structure classes can be distinguished by their capacity to interact with the surrounding extracellular matrix by degrading it. One such structure is podosomes that are generally present in monocytic originated cells that have differentiated in the tissue into macrophages. In specific detail, these podosomes contain F-actin stress fibers and several actin-binding proteins that are surrounding by a ring-like structure of integrins. Another type of structure is invadopodia, which can be characterized in their composition as similar to podosomes, but

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only occur in malignant cells (Linder 2007), where they emerge as more punctual, finger-like projections into the extracellular matrix and potentially endow these cells with differences in mechanical stability (Albiges-Rizo et al. 2009). Adhesive structure formation is controlled temporally and locally by numerous different proteins, and it is presumed that adhesion formation is initiated by the activation of the integrin family of extracellular matrix receptors. The interaction of integrins and the extracellular matrix environment leads to the controlled assembly of intracellular signaling sites within the regions, where the cytoplasmic integrin domains are clustered. These signaling sites recruit adaptor proteins, kinases, phosphatases and other cell surface receptors. The activation of integrins causes fast alterations of the associated cytoskeleton in an indirect manner that thereby facilitates mechanically the maturation of adhesion sites. In a similar way, the disassembly of these cell adhesions is also closely coordinated in terms of space and time and is essential for ensuring effective cell migration. Most of what is momentarily understood in the localization and turnover of cell–matrix adhesions has been characterized by the visualization of protein dynamics with a variety of microscopy techniques. However, most studies to date have been performed with cells plated on 2D surfaces. However, there are countless questions that have not yet been answered regarding the characteristics and dynamics of cell–matrix adhesions in intricate 3D matrices. In fact, the actual importance of various adhesion classes specified in cells located on 2D matrices for those encountered in cells in 3D environments is still questionable. This chapter explores how multiple techniques that are frequently utilized to investigate issues of adhesion patterns in living cells are employed and reviews specific results obtained by using these techniques. Finally, these adhesion sites that couple cells with their adjacent cells or with the surrounding extracellular matrix made of large complexes with multiple proteins, all of which can mechanical couple and thereby sense the matrix mechanical properties of their microenvironment (Bershadsky et al. 2003; Chen et al. 2004; Geiger et al. 2001; Sastry and Burridge 2000). The challenging interaction between the mechanical function of cell adhesions and their “instructive role”, which manifests itself through the activation of various signaling pathways, is conveyed by an array of proteins collectively referred to as “adhesome.” The combined action of the components of the adhesome essentially impacts across all cell functions encompassing cell morphogenesis, proliferation, viability, migration and differentiation (Berrier and Yamada 2007; Streuli 2009; Thiery 2003; Vicente-Manzanares et al. 2009). Cell–matrix adhesions range from small, short-lived focus contacts to punctiform, finger-based projections referred to as invadopodia.

4.2

Focal Adhesions and Focal Adhesion Proteins

Approximately 40 years after vinculins identification as a component of focal adhesions and the adherens junctions of cells, a huge effort of research has been put into the investigation of vinculin, including its activation, regulation and function (Bays and DeMali 2017). In this chapter, the present knowledge of the function of vinculin in cell–cell and cell–matrix adhesions opens up. The focus is on the recruitment, activation and regulation of vinculin. However, another recent insight is how vinculin reacts to integrin- and cadherin-containing adhesion complexes and exerts force on the underlying cytoskeleton. In parallel, topics of discussion include the role of vinculin in the attachment and restructuring of the actin cytoskeleton. Tissues in intricate multicellular organisms consist only of one or more cell types. Cells with epithelial or endothelial roots connect to adjacent cells over cell–cell contacts and attach to the subjacent basement membrane by cell–matrix coupling. Both kinds of adhesions are crucial for the development of embryos, the rebuilding of tissues, the migration of cells and many other homeostatic

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functions. The misregulation of adhesion sites results in a wide array of diseases including cancer, diabetes and cardiovascular disease. The process of cell adhesion undergoes continuously restructuring due to receptor engagement, receptor clustering and the dynamic turnover of adhesion receptors, such as integrins. These integrins are found strongly assembled within cell matrix adhesions, such as focal complexes and focal adhesions. The focal complexes represent small transient adhesions on the cell circumference that can increase to focal adhesions that become then bigger and more stable structures. In opposite, areas where cells adhere to adjacent cells are referred to as adherens junctions and are augmented with transmembrane adhesion receptors, referred to as cadherin. The pivotal role of adhesion receptors is played by the acquisition of proteins connecting the adhesion receptor to the actin cytoskeleton. Although a large number of proteins are present in adhesive complexes, vinculin, which is a cytoplasmic protein that can bind to actin, is accumulated in cell–cell and cell–matrix adhesions and is one of the most investigated in these areas. Vinculin possesses no enzymatic activity. It governs the adhesion by directly linking to actin, promotes the polymerization of actin and targets actin remodeling proteins. When vinculin is absent, cell–matrix and cell–cell adhesion are severely compromised, suggesting that vinculin is a key factor in human physiology.

4.2.1 Vinculin and Interacting Proteins Vinculin is a 116 kDa protein that has been identified in the laboratories of Benjamin Geiger and Keith Burridge as enriched in areas in which cells interact with each other and the subjacent medium (Geiger 1979; Burridge and Feramisco 1980). The cloning of the cDNA of vinculin showed that it encodes a protein with 1066 amino acids, and consecutive crystallization studies revealed that vinculin consists of eight anti-parallel a helical bundles divided into five different regions (Bakolitsa et al. 2004). The domains 1–3 (referred to as D1–3) of vinculin are organized in a tri-lobar head of 80 Å diameter (Molony and Burridge 1985) and possess a molecular weight of 95 kDa (Gimona et al. 1988). The vinculin head domain can interact with several proteins, such as a-actinin, talin, b-catenin, a-catenin and IpaA (Carisey and Ballestrem 2011). The head and tail domain of vinculin are linked by a 61 amino acid proline-rich linkage region that encompasses the residues 837–878 (Bakolitsa et al. 2004). These linker regions interact with the vasodilator-stimulated phosphoprotein (VASP) (Brindle et al. 1996), ponsin (Mandai et al. 1999), Arp2/3 (DeMali et al. 2002) and vinexin (Kioka et al. 1999). Finally, the vinculin tail (amino acids 879–1066) consists of a 30 kDa spiral bundle with five helices interconnected with short loops (3–8 residues) (Bakolitsa et al. 2004; Groesch and Otto 1990). The tail has binding sites for the vinculin head domain, actin, paxillin and acidic phospholipids (Fig. 4.1).

4.2.2 Activation of Vinculin In cells, vinculin can be detected in two vastly different conformations: Firstly, it can be found as an open and hence active protein form, and secondly, it can be a closed and hence auto-inhibited protein state, where the head domain of vinculin interacts stably with its tail domain. In this head-to-tail connection, the access of vinculin-binding proteins within this hidden region seems to be no longer possible. In fact, vinculin FRET probes, which are based on the two different conformational states of vinculin, revealed that vinculin is present in its active and extended conformation in focal adhesion sites, whereas vinculin can be found strongly folded by the interaction of its head and tail region and hence in its inactive form throughout the cytoplasm (Chen 2005).

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Fig. 4.1 Structure of vinculin, including domains and binding sites for vinculin interacting proteins and the internal head to tail binding regions of vinculin

Several hypotheses have been presented to elucidate how vinculin is triggered within the cell. The close bond between vinculin head and tail is considered too strong to be broken by a single ligand. In fact, the tail establishes two contacts with the head and one with the linker having a total Kd < 1 nM (Bakolitsa et al. 2004; Cohen et al. 2005). This close interaction culminated in the suggestion of a combined activation pathway where two or more ligands are necessary to alleviate intramolecular head–tail interactions (Fig. 4.2). In such a model, the linkage of actin to the tail and talin, a-actinin or

Fig. 4.2 Head-to-tail domain interaction of vinculin can be measured using FRET. The upper image shows the basic structure of vinculin. The intermediate image displays a typical vinculin tension sensor that can be employed for FRET. The lower two images show a high (lower left) and a low FRET (lower right) signal. The low FRET signal of vinculin is observed, when the vinculin molecule is stretched out and both fluorescent probes are increasingly distant from one another

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a-catenin to the vinculin head encourages an openness in conformation (Chen et al. 2006; Janssen et al. 2006; Bois et al. 2006; Peng et al. 2012; Izard et al. 2004). The molecular dynamical modeling has revealed how this mechanism can be activated. Hence, it can be proposed that talin can interact with vinculin head through a linkage that is driven by the hydrophobic surface interactions. This interaction makes it possible to free the vinculin head domain from the tail root and supports conformational alterations which enable the talin to fit completely into the center of the vinculin head domain (Golji and Mofrad 2010; Golji et al. 2011). There is other evidence to the effect that a single ligand is sufficient for vinculin to acquire an openness of conformation. Talin or a-actinin binding has been seen to be able to support alone the induction of conformational alterations that can break the connection of the vinculin head from the tail in vitro by a process that is known as the conversion of the helical bundle (Izard et al. 2004). But this model is mainly derived from experiments with purified vinculin head domain D1 and tail. It is now known that the Vinculin head connects the tail with a 1000 times higher affinity than the D1 domain on its own (Cohen et al. 2005). The activation of vinculin by a solitary ligand in the context of the whole molecule or within the cell therefore probably is not feasible. However, more recent investigations indicate that factors other than protein binding are able to affect the activation of vinculin. Molecular dynamic simulations, for instance, indicate that the phosphorylation of vinculin at Y100, Y1065, S1033 and S1045 activates the cell and stimulates the attachment of talin and actin (Golji and Mofrad 2010; Golji et al. 2012). There is other evidence to suggest that force encourages the activation of vinculin. To substantiate this claim, the force initiates the activation of conformational transitions in vinculin. By the opposite, a loss of tension results in the fast inactivation of vinculin (Grashoff et al. 2010; Carisey et al. 2013). Lastly, a third option identified is phosphorylation which improves the mechanical activation process and vice versa (Golji et al. 2012). In line with this notion, stretching uncovers tyrosine phosphorylation sites in other proteins, such as p130 Cas (Janostiak et al. 2011, 2014) and the binding sites of vinculin with talin (del Rio et al. 2009). For instance, vinculin activation is probably more ingenious than the combined activation or bundle inversion models suggest.

4.2.3 Role of Vinculin in Cell–Matrix Adhesions The cell–matrix adhesions or focal adhesions accumulate a wealth of adhesion receptors, including integrins. More than fifty proteins seem specifically to be targeted in the integrin cytoplasmic tail (Zamir and Geiger 2001). A more recent investigation carried out using super-resolution microscopy shows that these proteins are located in 3D nanodomains. The detected areas are: a membrane-applied integrin signaling layer, an actin-binding and force-transmitting intermediate layer and a top actinregulating layer (Kanchanawong et al. 2010). Vinculin is located in the signal layer, but is quickly transferred to the actin-binding layer when autoinhibitory head–tail interactions are alleviated (Case et al. 2015). At the actin-binding level, vinculin eases the acquisition of supplemental proteins controlling focal adhesion dynamics and permitting efficacious cell migration. Thereby, the function of vinculin in the process of cell migration is presented below. Since migration is strongly susceptible to the reordering of the actin cytoskeleton, a short discussion on the relationship between vinculin and F-actin is provided in this chapter. Finally, the mechanisms for the effective regulation of vinculin in focal adhesion are described.

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4.2.3.1 Vinculin Functions in Cell Migration Vinculin-depleted cells possess reduced migratory capacities (Xu et al. 1998a; Mierke et al. 2010). In specific detail, the migration of cells can be divided into four major essential steps: The first step encompasses the exertion of a protrusion of the leading edge of the cell, the second step involves the adhesion of the cell to its underlying substrate, the third step is the traction force generation that helps the cell to propel itself forward in a specific direction and the fourth and last step is the deadhesion of older adhesions at the rear of the cell in order to move the entire cell body forward. In all these steps, vinculin fulfills a crucial function. The Protrusion of the Cell at Its Leading Edge At the cell’s leading edge, vinculin is present in first adhesions (Zimerman et al. 2004). These emerging adhesions contain a large amount of vinculin linked to the Arp2/3 complex, which is a powerful nucleator of polymerization of actin (DeMali et al. 2002). The interaction between vinculin and the Arp2/3 complex ensures that the polymerization of actin is initiated at the newly assembled adhesions (DeMali et al. 2002; Chorev et al. 2014) and thereby provides a strong linkage between the integrins on the cell surface and machinery promoting actin polymerization. Thereafter, these newly assembled adhesions induce the enhanced protrusion of the entire membrane of the cell in the direction of migration (DeMali and Burridge 2003). However, it is not yet clearly known in great detail how the linkage of vinculin with the Arp2/3 complex is spatially restricted to the leading edge of the cell. In fact, there exists the hypothesis that phosphorylation processes are performing the regulation of their localization. Vinculin at Y1065 is highly phosphorylated in emerging nascent adhesions, and phosphorylation at Y1065 controls its attachment to the Arp2/3 complex (Moese et al. 2007; Mohl et al. 2009). In addition, mutant deficient of vinculin cannot attract the Arp2/3 complex or facilitate the protrusion of lamellipodia. As a result, the tyrosine phosphorylation-based recruitment of the Arp2/3 complex to vinculin seem to be a possible regulatory mechanism to provide the protrusion of the plasma membrane.

4.2.3.2 Vinculin Functions in Cell–Matrix Adhesion Directly after the cell’s leading edge, vinculin fulfills a key role in the maintenance of the focal adhesions. In fact, early experimental evidence leads to the assumption that cells exhibiting large focal adhesions overexpress vinculin (Rodriguez Fernandez et al. 1993), whereas cells that lack vinculin build up focal adhesions that become smaller and fewer (Mierke et al. 2008; Xu et al. 1998a, b). It has been exhaustingly investigated how vinculin regulates the size and the number of focal adhesions. The enrollment of vinculin to talin causes a stabilization of focal adhesions and drives the clustering of integrins and the overall extension of focal adhesions (Cohen et al. 2006; Humphries et al. 2007). Vinculin is known to directly facilitate the activation of integrins by its interaction with talin (Humphries et al. 2007; Ohmori et al. 2010; Nanda et al. 2014). Studies of how this happens point to talin being diverted from the cytoplasm to the plasma membrane when a cell signposted elevated integrin activation. The Rap1-interacting molecule (RIAM) regulates the relocalization of talin (Han et al. 2006), whereas the binding of vinculin to talin causes the disruption of RIAM that then enables transient RIAM-positive emerging adhesions to convert into vinculin-rich and further change toward mature and strong focal adhesions (Lee et al. 2013; Goult et al. 2013). Specifically, there is evidence that talin pinpoints vinculin to focal adhesions. While there are many trials encouraging this model, others indicate the involvement of other vinculin-binding partners. For example, talin may need support in situations where recruitment of vinculin is fast and robust, as for instance in cells experiencing force. Paxillin eases the enrollment of vinculin in cells undergoing external tension (Pasapera et al. 2010). In fact, the activity of myosin II enhances the

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binding of paxillin to vinculin. Based upon this background knowledge, a two-step manual model for the localization of vinculin in focal adhesions has been introduced (Pasapera et al. 2010). Paxillin binds vinculin in this model and delivers it to focal adhesions. In the next step, paxillin hands over vinculin to talin (Pasapera et al. 2010). In complement to the modulation of talin activities, vinculin acts to stabilize integrins by connecting to the actin cytoskeleton. How this happens is not fully clearly understood. Near the anterior edge of migrating cells, the vinculin tail domain collects backward flowing actin fibers (Thievessen et al. 2013). A result of the attachment to flowing actin filaments may be an elevated tension over the vinculin molecule. Tension enhancements can make vinculin to take on an open conformation and alter actin and focal adhesion dynamics on the anterior edge (Thievessen et al. 2013).

4.3

Transmission and Generation of Cellular Traction Forces

Focal adhesions close to the leading edge of migrating cells can transmit myosin-generated forces of the actin cytoskeleton toward the surrounding extracellular matrix, thus creating traction forces that draw the entire cell body to the front during cell migration (Thievessen et al. 2015). A crucial role for vinculin in the process of traction force generation and transmission has been initially demonstrated in focal adhesions that recruit vinculin and thereby boost its strength in response to force (Galbraith et al. 2002). Moreover, the key role for vinculin in the generation and transmission of traction force is based on investigations of migrating cells deficient of vinculin in 3D collagen fiber matrices (Mierke et al. 2008; Mierke 2009; Mierke et al. 2010; Thievessen et al. 2015). In specific detail, the loss of vinculin pronouncedly reduces the generation and transmission of traction forces and subsequently motility in 3D environments. It is interesting to mention that while a loss of vinculin reduces their motility in 3D matrices, fibroblasts from mouse embryos without vinculin display increased migration rates, when cultured on 2D substrates (Xu et al. 1998a, b). This evidence may imply that vinculin does not play a role in the generation of traction or that the generation of contractile forces is only necessary for the migration through 3D microenvironments, where the migration of cells may be sterically restricted. Nevertheless, direct measurements of the generation of traction force in the vinculin null mouse embryo fibroblasts have shown that vinculin is necessary for the generation of traction forces in 2D (Dumbauld et al. 2013). However, there may still be another migration type involved in the motility on flat 2D substrates. In line with this result, another role for vinculin in providing the generation of traction forces has been revealed. This research showed that focal adhesions have two types of tensile forces: a highly dynamic state in which primarily tugging traction forces are generated and a second state in which stable traction forces are generated and transmitted (Plotnikov et al. 2012). In both states of traction forces, vinculin is essential and even both tugging and stable traction forces are needed for the directional migration of cells. In line with these findings, cells with reduced expression of vinculin display a more random migration type than control cells (Rahman et al. 2016). Finally, it can be concluded that vinculin exhibits a key role not only in the generation of traction forces, but also in the directionality of the cell migration process. In fact, it is still an active research area how vinculin facilitates the generation and transmission of traction forces. While initial adhesions are developing, talin represents one of the first molecules enrolled for integrin-containing sites. In cell–matrix adhesions, talin is exposed to a tension that releases binding sites for vinculin (del Rio et al. 2009). The elongation of talin induces conformational changes in vinculin that strengthen F-actin anchoring and thus enable the formation of additional connections between integrins and the actin cytoskeleton (Ciobanasu et al. 2014). The final consequence of this is improved integration of clustering of integrins and the maturation of focal

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adhesions (Humphries et al. 2007; Hirata et al. 2014). Consequently, vinculin facilitates the generation of traction force through the stabilization of the linkage of integrins with the actin cytoskeleton.

4.3.1 Breakdown of Cell Adhesions at the Rear End Considering the critical roles of vinculin in controlling the activation of integrins and initiating the focal adhesion assembly, in turn, the disassembly of focus adhesions may entail a loss of vinculin or a reduction of its activation. How vinculin undergoes inactivation inside focal adhesions is still not yet discovered. There is a number of indications that phosphatidylinositol 4,5-bisphosphate (PIP2) and calpain have an important role to play. PIP2 binding triggers a conformational shift which diminishes the vinculin tail region of the binding to actin (Chandrasekar et al. 2005; Saunders et al. 2006). To continue fostering this hypothesis, the increase in PIP2 content in cells induces the removal of focus adhesions (Chandrasekar et al. 2005). Another mechanism to encourage focal adhesion degradation occurs in the interruption of vinculin-talin interactions. Calpain, which is a calcium-dependent protease, splits talin and thus helps to reduce focus adhesions. If the calpain cleavage site in talin is mutated, the protease becomes inactive. The vinculin sticks longer in the focus adhesions, and disassembly is prevented by this (Franco et al. 2004). Therefore, a loss of vinculin, vinculin that binds to actin, or vinculin that binds to talin, increases the turnaround of focal adhesions.

4.3.2 Interaction of Vinculin and Actin in Focal Adhesions The binding of vinculin to F-actin is crucial when it comes to cell–matrix adhesion. Disturbances in the interaction of vinculin and F-actin influence cell morphology, cell adhesion, cell motility and cell stiffness (Ezzell et al. 1997). The underlying reason for such failures is an inability to sustain, transduce and generate forces (Mierke et al. 2008, 2010; Thievessen et al. 2013; Thompson et al. 2014). It is explored in the following how vinculin facilitates the binding to F-actin and how this specific binding event can induce crucial processes. Among the initial investigations of vinculin is the notion that vinculin can bind to F-actin (Burridge and Feramisco 1980). Admittedly, the vinculin–actin boundary was strongly discussed. At first, the vinculin binding to F-actin was rebuilt using negative dye electron microscopy and diffraction data (Janssen et al. 2006). In specific detail, the F-actin has been hypothesized to interact with two distinct regions within the tail domain of vinculin, such as in the upper (amino acids 925–952) and lower vinculin monomer site (amino acids 1050–1056) (Janssen et al. 2006). These predicted residues of vinculin to interact with F-actin have been confirmed by others as vinculin-F-actin interaction regions (Cohen et al. 2005; Janssen et al. 2006; Shen et al. 2011; Jannie et al. 2015). Nevertheless, other investigations turned out, when residues outside the upper and lower monomer are mutated, and the vinculin binding to F-actin is significantly perturbed (Thievessen et al. 2013; Thompson et al. 2014). In conclusion, these different findings enlightened that the binding of vinculin binding to Factin is far more complicated and seems to involve multiple sites within vinculin being essential for the functional interaction. Due to the binding of vinculin, diverse impacts on actin have been detected. Based on this interaction, it seems to be widely accepted that vinculin and the bundled actin filaments interact with one another (Shen et al. 2011; Tolbert et al. 2014; Zhang et al. 2004; Tolbert et al. 2013). In addition, it has been revealed that vinculin is able to alter the actin bundles (Wen et al. 2009) and even induces the assembly of new actin bundles (Wen et al. 2009). Besides this function, vinculin caps actin filaments (Le Clainche et al. 2010) and nucleates the de novo polymerization of new actin filaments

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(Le Clainche et al. 2010; Wen and Janmey 2011). At the end, vinculin is able to promote the attraction of actin modifying proteins including the vasodilator-activating phosphoprotein (VASP protein), which acts as an anti-capping protein (Brindle et al. 1996) and the well-known Arp2/3 complex that acts as an actin nucleator for the branching of new actin filaments (DeMali et al. 2002). These functional features of vinculin make it suitable to serve as an ideal candidate for the generation of new actin filament assemblies and the alteration of the existing actin filament structures.

4.3.3 Vinculin Regulation in Cell–Matrix Adhesions How the regulation of vinculin with in cell–matrix adhesions is performed is still not yet clearly known. It has been hypothesized by a growing number of experimental studies that the tensional state plays a crucial role (Grashoff et al. 2010; Carisey et al. 2013; Pasapera et al. 2010; Plotnikov et al. 2012; Ciobanasu et al. 2014). Other additional factors seem to be the processes of phosphorylation and the oligomerization of the vinculin determining its function. What Role Plays the Intracellular Tension? In fact, a new topic is that vinculin is governed by tension. The force over the vinculin encourages the assembly of focal adhesion and promotes their own growth (Grashoff et al. 2010). However, it is still not clearly revealed how tension affects the activity of vinculin. The tension in vinculin has the potential to induce conformational changes that could promote interactions with its binding partners (Golji and Mofrad 2010). Similarly, conformational alterations within the binding partners may also foster vinculin binding. For example, the force exerted on the talin activates the unfolding of its rod domain and shows binding sites for vinculin. Vinculin linkage to talin dams talin in an active conformation constituting focal adhesions (Bachir et al. 2014; Atherton et al. 2015a). For this reason, tension-dependent conformational alterations govern the binding to vinculin.

4.3.4 Phosphorylation of Vinculin Former studies point out that modifications in the tyrosine phosphorylation of vinculin are associated with a loss of cell–matrix adhesion. Subsequent trials demonstrated that Y100 and Y1065 of vinculin are phosphorylated by Src kinase during development and maturation of focal adhesion (Sefton et al. 1981). The mutation of one of these tyrosine residues does not affect the targeting of vinculins to focal adhesions, but rather impedes cell spreading, migration of cells and the generation of traction forces (Zhang et al. 2004; Auernheimer et al. 2015; Kupper et al. 2010). How the tyrosine phosphorylation of vinculin affects its function is still a major field of research. Based on the full-length crystal structure of vinculin, the Y100 residue is freely accessible in the solvent, while Y1065 is closed by the linker domain (Borgon et al. 2004). More precisely, it has been hypothesized that both residues, such as Y100 and/or Y1065 of vinculin, represent crucial sites of the interaction of vinculin with phospholipids (Ito et al. 1983; Diez et al. 2009) and enzymes altering actin (DeMali et al. 2002; Moese et al. 2007). It is necessary to note that the phosphorylation of vinculin at these tyrosine residues has no impact the interaction with actin. Nevertheless, the phosphorylation changes the protein binding through the conformational alteration of vinculin. In line with this finding, mutation of the tyrosine residues Y100F and Y1065F abolishes the interaction between the head and tail domain of vinculin (Auernheimer et al. 2015; Diez et al. 2009; Huang et al. 2014), and consequently, the phosphorylation of vinculin at these sites supports the activation of vinculin by adapting an active conformation (Golji et al. 2012; Auernheimer et al. 2015). Simulations of the

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molecular dynamic of vinculin lead to the hypothesis that the phosphorylation causes the remodeling of charged residues within the head domain of vinculin that are located at the interaction sites the D1 region and the tail domain (Golji et al. 2012). Finally, all these studies support the hypothesis that the phosphorylation at the two tyrosine residues Y100 and Y1065 represents a critical determinant for the function of vinculin. Similar to the analysis of the tyrosine phosphorylation, the serine phosphorylation of vinculin has been examined. It has been discovered by in vitro phosphopeptide mapping that two sites, such as the serine residues S1033 and S1045 within the tail domain of vinculin are a substrate of a specific kinase, protein kinase C (PKC) (Ziegler et al. 2002). The phosphorylation of the residue S1033 of vinculin is mostly investigated. In specific detail, cells that express the S1033A phosphodeficient mutant of vinculin are more bendable and less able to generate tensile forces (Auernheimer and Goldmann 2014). Nevertheless, it is still highly discussed how the phosphorylation at the residue S1033 of vinculin can alter the structure of the entire vinculin molecule. With computational simulations, it has been detected that the phosphorylation of S1033 of vinculin may somehow weaken the interaction between the head and the tail domains of vinculin. Hence, it can be proposed that the serine phosphorylation of these residues induces a conformational shift of vinculin similarly to the tyrosine phosphorylation (Golji et al. 2012). However, the validation of this hypothesis is still not provided and requires further experiments.

4.3.5 Interaction of Vinculin and PIP2 Similar to the phosphorylation of vinculin, there is some still growing evidence that the phosphatidylinositol 4,5-bisphosphate (PIP2) contributes to the regulation of the function of vinculin. More precisely, the tail domain of vinculin binds PIP2 and hence induces a conformational shift in vinculin that frees the head domain of vinculin to facilitate the binding of talin (Scott et al. 2006; Diez et al. 2008; Wirth et al. 2010). As an alternative way, PIP2 acts together with a ligand, such as talin or a-actinin and/or F-actin, of the head domain of vinculin that is required for the activation of vinculin in vitro (Bakolitsa et al. 2004; Izard et al. 2004; Thompson et al. 2017; Bakolitsa et al. 1999; Weekes et al. 1996). At the end, vinculin capacity to assemble to oligomers seems to be critical. Vinculin’s tail domain binds to F-actin and thereby induces the oligomerization of PIP2. In the course of vinculin’s binding to actin, it assembles to dimers that drive the dimer formation of PIP2 and even other highordered structures, such as trimers and tetramers (Chinthalapudi et al. 2014, 2015). Any physiological implications of the diverse oligomeric states and the findings of PIP2 binding still require additional analyses.

4.4

Vinculin Functions in Cell–Cell Adherence Junctions

The functional role of vinculin in cell–cell adhesions is not yet precisely known than the function in cell–matrix adhesions. Nevertheless, developmental investigations revealed that vinculin is highly required in the proper function of cell–cell adhesions. In fact, mice, in which vinculin has been genetically knocked-out, are embryonic lethal at day 10.5 (Xu et al. 1998a). In specific detail, these mice display multiple developmental abnormalities, such as closure defect of the neural tube, and their hearts show failure in bringing them together properly (Xu et al. 1998a). These two different phenotypes may be based on the impaired cell–cell adhesive interaction. In the same way, in 49% of the mice suffering from a cardiomyocyte-specific deletion of vinculin, there is a spontaneous mortality in the course of the first three months after birth (Zemljic-Harpf et al. 2007). Before the

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beginning of death, ultrastructural analysis on the hearts of these mice yielded aberrant adherens junctions (Zemljic-Harpf et al. 2007). Based on these finding, vinculin’s presence in cell–cell adherence junctions seems to be essential for multiple physiological functions. It is still largely elusive how vinculin is assembled into cadherin junctions. It has been hypothesized that a-catenin manages the recruitment of vinculin to these sites. The hypothesis comes from the experimental result that cells deficient of a-catenin or in hearts lacking a-catenin display no vinculin in their cell–cell adherence junctions (Sheikh et al. 2006; Watabe-Uchida et al. 1998). But cells expressing vinculin mutants incapable of binding to b-catenin while retaining the ability to bind to a-catenin do not locate on cadherin contacts. Therefore, b-catenin plays an extremely important role (Peng et al. 2010). In continuing to underpin this concept, vinculin cannot be detected in adherence junctions within cells deficient of b-catenin (Ray et al. 2013). These observations indicate evidence that the recruitment of vinculin to cell–cell interactions can be a multi-stage procedure comprising both a-catenin and b-catenin. Two actual hypotheses for the localization imply: Firstly, bcatenin facilitates the recruitment of vinculin and passes it on to a-catenin, or secondly, b-catenin mediates the recruitment of vinculin, whereas a-catenin contributes to the stabilization of this interaction. However, there is still more research effort needed to figure out which hypothesis can be confirmed. Most of the first trials that investigated the recruitment of vinculin used cell-cultured cells, which were not subjected to force or had a low force effect. Follow-up research revealed that force has a major role in recruiting proteins for the cadherin-containing adhesion complex. Vinculin is known to be located in cadherin-containing sites due to myosin II-driven contractility (Huveneers et al. 2012), myosin VI-driven contractility (Leerberg et al. 2014) and externally exerted tension (Barry et al. 2014; Thomas et al. 2013). A number of various proteins interfere with a force-based recruitment of vinculin. Among these are a-catenin that is subject to a force-dependent conformational alteration and thereby uncovers vinculin-binding sites (Yao et al. 2014a, b; Yonemura et al. 2010) and Eplin, which is a protein that causes the exclusion of vinculin from cell–cell adherence junctions, when it is impaired. Finally, a-actinin has been reported to localize vinculin toward cell–cell adherence junctions due to tension (Kannan and Tang 2015). Decreased expression of a-actinin or its upstream activator synaptopodin impairs the tension-based targeting of vinculin to cell–cell adherence junctions (Kannan and Tang 2015). However, it is uncertain about whether a-actinin targets vinculin directly or indirectly via a a-catenin-based interaction. Another major factor in the recruitment of vinculin into cadherin-containing cell–cell adherence junctions represents the tyrosine phosphorylation. Vinculin has been shown to be phosphorylated at its tyrosine residue Y822 due to the force on E-cadherin but not on integrins. Vinculin mutant Y822F cannot bind b-catenin and is not targeted to cell–cell adherence junctions, which leads to the hypothesis that the phosphorylation of vinculin at Y822 seems to be impact the conformation of vinculin (Bays et al. 2014). This concept is underpinned by employing super-resolution microscopy to determine the localization of vinculin in cadherin-rich cell–cell contacts and a FRET biosensor approach that monitors the confirmation of vinculin (Bertocchi et al. 2017). A phosphomimetic Y822E passes through an alteration in FRET and repositions itself in an actin-binding layer, pointing to a conformational shift in vinculin (Bertocchi et al. 2017). Finally, the tyrosine phosphorylation of the Y822 residue of vinculin seems to effectively regulate the precisely targeting of vinculin localization through the determination of the conformational phenotype. There are functions identified for vinculin in cell–cell adherence junctions. The targeted removal of vinculin from cell–cell adherence junctions, without affecting its function in cell–matrix adhesions, reduces cell–cell adhesion and leads to a loss of E-cadherin from the cell surface (Peng et al. 2010). The vinculin binding to b-catenin is decisive for this outcome. On support of this thought, mutant versions of vinculin, which cannot interact with b-catenin, are not able to rescue the membrane

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expression of E-cadherin (Peng et al. 2010). This role recalls the function of vinculin in cell–matrix adhesions, in which vinculin fixes integrins in the cytoskeleton during the maturation of focal adhesion and prolongs the retention time of integrins in focal adhesions. Consequently, the function of vinculin in the stabilization of adhesion receptors seems to be highly conserved in cell–matrix and cell–cell adhesions. Besides the facilitation of adhesion, new findings indicate that the transfer of force represents another major function of vinculin in cell–cell connections. E-cadherin represents a mechanosensory, which transfers the external forces toward the actin cytoskeleton, and vinculin fulfills a prominent role therein (Le Duc et al. 2010). The evidence comes from experiments pointing out that mechanosensors are missing in cells without vinculin or expressing a mutant form of vinculin that cannot be phosphorylated in the Y822 residue (Bays et al. 2014). However, vinculin is probably not the only promoter of E-cadherin mechanotransduction, as other work proposes a part for a catenin and/or eplin in E-cadherin mechanotransduction and in the recruitment and retention of vinculin in cell–cell adherence junctions in cells subject to tension (Chervin-Petinot et al. 2012). It is highly speculative how vinculin performs the precise regulation cell–cell adhesive actions. A probable possibility is that vinculin regulates actin dynamics to link to the actin cytoskeleton. In strengthen this concept, VASP has been shown to interact with vinculin, which is required for vinculin’s regulatory function on the junctional actin assembly, when the cells are exposed to force (Leerberg et al. 2014). Alternatively, but not necessarily exclusive, vinculin gives a-catenin stability in a conformational state can bind to F-actin. In other words, a-catenin, not vinculin, is the most relevant link between the cadherin adherence junctions and the actin cytoskeleton.

4.5

Unanswered Questions Regarding Vinculin for Future Investigations

In a larger number of reports, the functional roles of vinculin within cell–cell and cell–matrix adhesions are usually described as separate units. However, a series of investigations indicate that balanced cell– cell adhesion and cell–matrix adhesion are crucial for correct development (McCain et al. 2012). In line with this assumption, there is significant cross talk occurring between the two splices with variations in adhesion/force transfer at one point influencing the variation at the other (DeMali et al. 2014; Weber et al. 2011). In fact, the binding of integrin to the matrix reinforces the adhesion provided by cadherin (Martinez-Rico et al. 2010). In the same way, increases in integrin-mediated traction forces are associated with an augmentation of the myosin-dependent tension at cadherin adherence junctions (Maruthamuthu et al. 2011; de Rooij et al. 2005). The reverse is also happening, and the tension on cadherins subsequently alters integrins. Cells that come into contact with their neighboring cells are capable of generating higher traction forces than single cells (Jasaitis et al. 2012; Mertz et al. 2013). In order to fully reveal how these interactions of these activities are precisely coordinated at these two different types of adhesion sites, more investigations are highly required. The adhesion/force transfer at cell–matrix adhesions is not based solely on alterations in the cell– cell adhesion/force transfer and the other way around. For instance, increasing the density of vascular endothelial cells deposited on a substrate increases cell–cell contact and reduces cell–matrix adhesion (Nelson et al. 2004). The mechanisms how cells can differentiate between the different functions of the specific adhesion site are not clearly known. The exertion of force toward cadherins, but not toward integrins, induces the activation of the Abl tyrosine kinase and thereby promotes the phosphorylation of the Y822 residue of vinculin (Bays et al. 2014). In addition, the degree of Y822 vinculin phosphorylation defines the amount of force transferred by cadherins. In contrary, vinculin phosphorylation at the residue Y822 has no impact on the transmission of integrin forces. Thus, the phosphorylation can be used to differentiate between the function of vinculin in cell–matrix and cell–

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cell adhesions. It is most probable that there are alternative mechanisms to synchronize the vinculin’s functions and that of vinculin’s-binding partners. Finally, there is still future effort required to figures out the fine detail of these major regulatory signaling pathways.

4.6

Mechanotransduction

The cells possess the capacity to sense and react to the mechanical cues, which seems to be essential for the proper development and the functioning of healthy tissues. There are multiple diseases that are based on either perturbed mechanics of the tissue, or perturbances of the capacity of cells to sense the mechanical environmental cues (Jansen et al. 2017). To a certain degree, the sensing can be seen at integrin-associated complexes assembling attachment sites of the cell toward the extracellular matrix microenvironment. The complex mechanical signal transduction processes of the extracellular matrix environment are presented in the following. It is explored how integrin-associated complexes contribute to the cellular sensing of these mechanical cues by point out to the associated molecular mechanisms of major adhesion molecules. In the end, the cellular mechanotransduction processes including mechanosensing and signaling aspects of key proteins within focal adhesions are highlighted. In the tissues of our body, the cells are exposed to various mechanical irritations, such as shear stresses from the blood circulation or stretching and compressive forces exerted by a number of tissues connected to muscle activity (Jaalouk and Lammerding 2009). In general, the cells are circumvented by an extracellular matrix environment consisting of multiple proteins, such as fibronectin, laminins and various types of collagens. Cells are able to directly fell the intrinsic mechanical cues of the surrounding extracellular matrix through the exertion of traction forces. The capacity of the cells to react to external force exertions, to explore and evaluate the mechanical features of the extracellular matrix and to generate and redesign the extracellular matrices, the cells need to adapt their functions in many areas of cellular performances (Jansen et al. 2015). For instance, to the stiffening of the extracellular matrix during various states of different diseases including cancer and fibrosis (Jaalouk and Lammerding 2009; Lu et al. 2012) or during the process of aging (Snedeker et al. 2014) can negatively influence cellular processes spanning over cell migration, differentiation and proliferation. As an alternative strategy, abnormal intracellular signals impairing the ability of cells to detect and react to extracellular mechanical stimuli may also cause disease states such as cancer (Jaalouk and Lammerding 2009). The feature of cells to sense and react to external mechanical stimuli is referred to mechanotransduction. The process of mechanotransduction necessitates the capacity to detect the external environmental forces or biomechanical phenomena and the transfer of this information initiating the onset a distinct intracellular signaling responses. The cytoskeleton plays a critical role in mechanotransduction by linking cellular compartments, such as the cell’s cytoskeleton and the nucleus (Cho et al. 2017), to the machinery of force-sensing. Specific mechanical terms are defined briefly: Mechanotransduction means the overall process of how the cells sense mechanical cues and convert them to biochemical and intracellular responses. Mechanoresponse is the special response of cells toward mechanical cues, such as elevated phosphorylation of proteins, alterations in the transcription of genes and alterations in the behavior of cells. Mechanosensing means the cellular process of sensing a mechanical cue. Mechanosignaling is an intracellular signaling phenomenon that occurs as a reaction to a mechanical trigger.

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Mechanosensitive means that a protein or a specific domain of a protein detects to force, whereby it alters its conformation in a force-dependent manner. Mechanotransmission is the act of transmitting a force, such as the transmission of intracellular forces to the outside environment to explore the extracellular matrix properties, or vice versa. A highlight is on the complexity of the mechanical phenotypes of the extracellular matrix that are sensed by the cells. Cells contain multiple force-sensing machines ranging from force-sensitive channels (Kobayashi and Sokabe 2010) to cell–cell adhesions (Huveneers and de Rooij 2013); however, a special focus is on mechanotransduction processes at integrin-associated complexes. These complexes can directly couple the extracellular matrix to the cell’s actin cytoskeleton, and thereby, they represent major contributors of the mechanotransduction processes in a cell. The relevance of the integrin-associated complex protein dynamics is discussed and correlated with the cell’s functional role in mechanosensing and mechanosignaling issues.

4.6.1 Besides Functioning as a Passive Supporter for Cells, the Extracellular Matrix Can Fulfill Additional Tasks Besides a passive scaffold representing ligands for cell–matrix adhesion receptors for the adhesion of the cells, the underlying substrate, the extracellular matrix possesses various types of mechanical stimuli and even offers dimensionality for the cells. An emphasis will be placed on the role of the physical properties of the extracellular matrix environment in the light of cellular mechanosensors. There are large differences between the 2D and 3D mechanosensing mechanisms that are also addressed below (Doyle and Yamada 2016; Hogrebe et al. 2017).

4.6.1.1 Mechanics of Extracellular Matrix Scaffolds 2D substrates that have been used for cell culturing experiments for many decades revealed that various cell types can able to react to the nearby mechanical properties such as the elastic properties of the underlying surface medium. The elasticity of these substrates has been shown to impact multiple fundamental cellular functions and processes, such as the motility of a wide range of different cells, the proliferation, differentiation and death of cells (Engler et al. 2006), and even the phenomenon of axon routing of neuronal cells (Koser et al. 2016). Besides the elastic properties of the matrices, such as the purely elastic polyacrylamide gels, that are crucial for the alteration of cell behavior, tissues display not only purely elastic behavior and display instead also a stress relaxation behavior (Chaudhuri et al. 2016). In fact, the spreading behavior of fibroblasts on soft flat substrates is increased when the remodeling capacity of the extracellular matrix is enhanced by imposing stress relaxation on the underneath interface or raising its viscous properties (Chaudhuri et al. 2015). Hydrogels that exhibit more or less stress relaxation behavior govern the fate of stem cells, which is not dependent on other parameters, such as the elasticity or density of ligands (Chaudhuri et al. 2016). These combined features, elasticity and the viscous properties referred to as viscoelasticity, appear to be partially critical for environments closer to in vivo tissue environments, such as collagen fiber frameworks, which exhibit highly viscous properties in cell-relevant time periods (Mohammadi and McCulloch 2014). It becomes even more complex when the mechanical properties of extracellular matrix networks are taken into account, since these networks have more than one stiffness value because they stiffen under external load (Storm et al. 2005). This strain stiffening of extracellular matrix meshes can also occur under the impact of cell forces, which renders the entire meshwork stiffer (Jansen et al. 2013). Strain-stiffening can imply that cells measure a stiffer environment relative to the initial polymerization conditions in vitro and can generate a favorable positive feedback loop for the increased

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generation of cellular forces (Hall et al. 2016). The resulting stiffness of the microenvironment can thus be defined in terms of a cumulative set of factors: extracellular matrix framework mechanics, cell density and the capacity of these cells to react and apply traction. In fact, low traction cells, such as neuronal cells, will use this mechanism to stiffen the extracellular matrix considerably less than cells with high traction force potential, such as fibroblasts by employing this strain-stiffening feedback loop (Georges et al. 2005). The forces also spread more widely in extracellular matrix meshes than in traditional polyacrylamide or PDMS substrates on account of their fibrous character (Ma et al. 2013; Rudnicki et al. 2013). Hence, the mechanical interaction distances between different cells within this scaffold increase pronouncedly (Reinhart-King et al. 2008; Sapir and Tzlil 2017).

4.6.1.2 What Microenvironment Can a Cell Feel? Besides mechanical properties, the extracellular matrix delivers dimensionality. Under in vivo conditions, the extracellular matrix may exhibit various structural architectures. For instance, eye collagen can polymerize to thin fibrils of about 30 nm in, whereas collagen in tendons is assembled to form relatively large bundles of several micrometers in diameter (Fratzl 2017). Hence, these results lead to the hypothesis how the dimensionality is important for cell functions and their structural arrangement. Embedded in a meshwork of fibers, cells have the option to directedly connect to several fibers of the extracellular matrix environment and moreover to sense the functional and mechanical properties of it. Thick fibers, on the other side, could rather remind of a 2D surface. In diseases involving migration of cells, such as cancer, cell migration can be performed along microtracks that possess both, features from 2D and 3D microenvironments (Rahman et al. 2016). Specifically, experiments with fibroblasts, which migrate on 2D line structures or in 3D collagen networks, revealed that the cells perceive under both conditions a 1D situation (Doyle et al. 2009, 2012). Since this variation of cell behavior is a function of the type of dimensionality of a cell, the question is whether the molecular mechanisms of mechanotransduction deviate accordingly. In specific detail, it can be raised the following question of whether the mechanisms employed by cells cultured on top of 2D substrates are similarly important for the cells that are embedded within in a 3D extracellular matrix microenvironment? The morphology of cells is pronouncedly altered when they are experiencing a 3D microenvironment. Adhesive cells cultured on 2D surfaces usually spread to a thin pancake-like structure, whereas cells embedded in 3D extracellular matrix scaffolds display multiple morphologies, including spindle-like or roundish shapes (Hakkinen et al. 2011). Although microtubules have proven to be negligible for cell propagation in 2D, they are necessary for 3D cell migration within collagen grids (Kraning-Rush et al. 2011; Rhee et al. 2007), even when this depends on the tension state of the grids. In 3D, actin stress fibers are lessened and focus adhesions are typically weaker than in 2D microenvironments (Hakkinen et al. 2011). Nonetheless, an uninterrupted remodeling of the extracellular matrix could improve cell spreading by boosting the regional ligand density and thus raising the probability of developing more integrin-associated complexes in 3D settings (Kubow et al. 2013). In reality, the fibrous character of the extracellular matrix can improve cell spreading provided the cells have the capacity to attract adjacent fibers (Baker et al. 2015). Of interest is that the focal adhesion composition is similar to the 2D conditions (Hogrebe et al. 2017), although it may require the accurate experimental parameters as the level of phosphorylation of focal adhesion proteins in 3D tends to be somewhat lower. On the downside, the focal adhesions accumulated in 3D are generally stronger (Doyle et al. 2012). In view of the complex nature of the integrin-associated complexes, it is essential to conduct follow-up experiments in order to clarify the source of this elevated stability. Beyond that, it is uncertain as to whether the binding kinetics of all focal adhesion proteins undergoes

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a modification in the course of a 3D environment. In 3D environments, a number of focal adhesion proteins apparently try to perform differently than in 2D (Fraley et al. 2010).

4.6.1.3 Integrin-Facilitated Cell Adhesions Integrin-associated complexes couple extracellular matrix proteins to the intracellular actin cytoskeleton through transmembrane receptors, referred to as integrins. Integrins with an a and a b chain (Hynes 2002) are heterodimeric proteins that have specific sites in the extracellular matrix such as RGD or LDV (Humphries et al. 2006). The extracellular head domain of integrins is the binding domain of the extracellular matrix (Hynes 2002), whereas the intracellular tail of integrin is linked to the actin cytoskeleton through several plaque accumulating proteins. The creation, maturation and dismantling of these plaques are strictly controlled with regard to time and space. Small integrinassociated complexes emerging at the leading edge are referred to as focus complexes, capable of developing larger focus adhesions (Vicente-Manzanares and Horwitz 2011). Focal complexes build up with no need for actomyosin-induced forces. They disintegrate quickly when no force is exerted, but as soon as they connect to the force vehicle, the focal complexes age toward focus adhesions. Specifically, in matrix-secreting fibroblasts, focal adhesions are able to differentiate into fibrillar adhesions assisting extracellular matrix biosynthesis and the structural remodeling (Zamir et al. 2000; Pankov et al. 2000; Georgiadou et al. 2017). The maturing of integrin-associated complexes comprises highly dynamical processes entailing the enrollment and the activation of certain proteins. At the same time as there is a deep insight into protein interaction systems in integrin-associated complexes, a limited knowledge of how this structure regulates cellular responses and how it is implicated in mechanotransduction mechanisms.

4.6.2 Mechanisms of Mechanotransduction Integrin-associated complexes provide a connection between the intracellular actomyosin meshwork and the surrounding extracellular matrix environment. Through their capability to govern extracellular matrix production and rearrangement, these cells define the physical and biochemical properties of the tissues on which they react and ultimately induce cell performance.

4.6.2.1 The Composition of the Force-Sensing Integrin-Associated Complexes Integrins operating with extracellular matrix attract a wide range of proteins that modify the binding to the actin cytoskeleton. A first collection on the basis of the published literature identified 156 components of the “adhesive” with 690 connections that is binding, activation and inhibitory interactions between the different compounds (Zaidel-Bar et al. 2007), even though much more constituents within the adhesome have now been discovered (Schiller et al. 2011; Schiller et al. 2013; Kuo et al. 2011; Ng et al. 2014). A most recently conducted study merged various proteomics datasets into a “consensus adhesome” that pinpointed 60 components depicting the core components of the integrin-associated complexes found on all surfaces of the integrin ligands (Horton et al. 2015). Proteomic investigations yielded valuable information on the complexity of integrin-associated complexes as signaling nodes. Almost half of the many focal adhesion proteins are phosphorylated in adhesions (Robertson et al. 2015). Among them, two subsets of phospho proteins were identified within integrin-associated complexes: some that are specifically phosphorylated as a reaction to the integrin extracellular matrix interference, and others that are contiguously phosphorylated, which are then recruited to integrin-activated complexes when the integrin extracellular matrix interference occurs. Integrin-associated complexes are naturally intricate entities with a high amount of plasticity

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in terms of their components. However, the proteins seem to be arranged hierarchically than just being placed randomly.

4.6.2.2 The Structure and Architecture of Focal Adhesions The super-resolution microscopy investigations revealed how the precise organization of focal adhesion structures on the nanoscale is achieved (Kanchanawong et al. 2010) and even determines molecular protein layers within mature focal adhesions. The nearest protein film to the integrins in the plasma membrane comprises the highly phosphorylated signal proteins focal adhesion kinase (FAK) and paxillin. The next nearby film is referred to as force transduction layer that consists of vinculin and talin that are termed adapter proteins coupling the integrins to the actomyosin cytoskeleton. Among these adapter proteins, talin can span over the entire layer. Thereby, talin’s head domain is associated to integrins and its rod binds actin, which can be located even up to 30 nm away from the integrins (Liu et al. 2015). In specific detail, the actin regulatory binding proteins including VASP, Zyxin and a-actinin and actin stress are located in the most internal layer that can even be located 60 nm distant from the integrins. These proteins can also interact with F-actin fibers. Based on two initial adhesome reports (Zaidel-Bar et al. 2007; Zaidel-Bar and Geiger 2010), the adhesome has been subdivided in specific layers, which help to identify the specific function of adhesome proteins due to their distinct localization (Geiger and Yamada 2011). For instance, kindlin is able to interact with integrins and paxillin (Theodosiou et al. 2016). Paxillin is found to be located exclusively uppermost signaling film nearby the cytoplasmic domain of the integrins, whereas kindlin can be counted to the same signaling layer. The ILK-PINCH-parvin complex represents another candidate that can be added easily. This protein complex is connected to integrins and F-actin fibers (Radovanac et al. 2013), thus seems to be an essential part of the force transduction layer. In order to reveal the 3D organization of mature focal adhesions, the super-resolution microscopy can be employed. Besides focal adhesion, other integrin-associated complex structures, such as focal complexes or fibrillar adhesions, are not yet analyzed. The molecular assembly of these structures can be altered during the maturation that is evoked by the interaction of actin and myosin, and consequently, they are differently composed and structurally organized. In precise detail, zyxin has be detected to be usually not present in focal complexes, whereas it can be found in focal adhesions and fibrillar adhesions (Zaidel-Bar et al. 2003) and tensin is identified to be accumulated in fibrillar adhesions (Georgiadou et al. 2017). Another issue is that the differences may even be larger in various cell types, such as neurons can assembly solely small point contacts, which seem to be similar to focal complexes due to their limited size (Myers et al. 2011), but they are still different to them, as these small point contacts can transmit traction forces to their microenvironment similar to larger focal adhesions (Athamneh and Suter 2015). However, whether they contain a similar molecular architecture to focal adhesions is not yet clearly revealed. 4.6.2.3 Transmission of Forces and the Molecular Clutch In the early stages of cell adhesion, such as in the formation of focal complexes, integrin-activated complexes recruited solely a small number of proteins, which are not ordered precisely. Afterward, to assemble more proteins in maturated focal adhesion is precisely regulated in a hierarchical manner, which facilitated by actomyosin-driven contractility (Zaidel-Bar et al. 2003; Ballestrem 2001; Balaban et al. 2001; Riveline et al. 2001). However, the specific mechanism that regulates the ordered recruitment of the focal adhesion proteins is still subject of ongoing research (Serrels and Frame 2012; Lawson and Schlaepfer 2012) and may even turn out to be cell type specific. The integrin function combined with the actin-associated force generator is decisive, as it governs the cell movement and is important for mechanosensor functions. This connection is referred to as the “molecular clutch” mechanism and is required for the mechanosensory process. At the periphery of

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the cell nearby the plasma membrane at the cell’s anterior leading edge, the polymerization of actin leads to a filamentous actin (F-actin) structural scaffold. The continuous polymerization of actin generates a reverse (synonymously termed retrograde) flow of actin (Case and Waterman 2015). The engagement of the actin filaments to integrin-associated complexes causes a reduction of this retrograde flow, whereas the still continuing polymerization of actin deforms the plasma membrane in the direction of the external environment, which finally cumulates in the exertion of specific membrane protrusions. Based on the myosin II-driven pulling forces, a cell can move itself toward other locations by the translocation of the entire cell body that leads to a forward movement in targeted directions. The molecular clutch associated proteins within focal adhesions, such as talin, vinculin and aactinin provide the functional coupling with the cell’s actin cytoskeleton and move in the same direction as the retrograde flow (Hu et al. 2007). Among these proteins, talin and vinculin, which belong to the force transducing film, seem to be major components of the molecular clutch (EloseguiArtola et al. 2016; Thievessen et al. 2013; Humphries et al. 2007). More experimental findings are in line with this interpretation, such as the cells lacking talin that cannot facilitate the formation of adhesions. Moreover, when the connection with the actin cytoskeletion is abolished, the focal complexes cannot mature (Elosegui-Artola et al. 2016; Atherton et al. 2015a, b; Zhang et al. 2008; Austen et al. 2015). In contrast, cells lacking vinculin are still able to assemble focal adhesions, in which the generation of forces and their transmission is impaired together with the cells’ inability to react to forces (Plotnikov et al. 2012; Dumbauld et al. 2013; Carisey et al. 2013; Holle et al. 2013; Rubashkin et al. 2014; Yamashita et al. 2014; Jannie et al. 2015). Since the operation of integrins with the contractile actomyosin apparatus is crucial for the analysis of substrate mechanics, the identical proteins governing the coupling are decisive for the mechanosensing mechanisms.

4.6.2.4 In the Process of Mechanosensing, Talin and Vinculin Connect the Actomyosin Cytoskeleton to the Integrins The acquisition of the mechanical input is a decisive initial step in the process of cell decision. In this initial step, triggering impulses such as stress or shear forces (Friedland et al. 2009) or cellular forces are transmitted to the matrix (Plotnikov et al. 2012). The two scenarios concern the well-known adapter proteins talin and vinculin. Talin and vinculin pass through both activation mechanisms to ultimately combine integrins with F-actin. Both proteins target focal adhesions when they are activated and both are maintained in an inactive, closed conformation by autoinhibitory linkages in the cytoplasm. Talin possesses an N-terminal integrin-binding four-point-one, ezrin, radixin, moesin (FERM) domain that is connected to a C-terminal rod domain, which consists of 13 helical bundles (known as R1-R13) ending up in a dimerization motif. An interplay between F3 of the FERM domain and R9 of the rod constitutes the autoinhibitory linkage inside talin (Calderwood et al. 2013; Goksoy et al. 2008; Goult et al. 2009). In vinculin, the tail domain (Vt) is retained in a forceps-like manner between the areas of the head (D1-D4) (Izard et al. 2004; Bakolitsa et al. 2004). Nevertheless, in vitro investigations carried out have demonstrated that the strength of this autoinhibition is significantly stronger for vinculin (Bakolitsa et al. 2004) than for talin (Goksoy et al. 2008). The mechanisms providing the activation of both proteins seem to be closely related. In their inactive state talin and vinculin can assemble a complex inside the cytoplasm (Bachir et al. 2014) that can be targeted toward the regions of the integrin–extracellular matrix linkages to drive the assembly of adhesions. An alternative pathway represents the GTPase Rap1 and its effector RIAM that are able to facilitate the targeting of talin toward the plasma membrane (Lee et al. 2009; Yang et al. 2014). In both scenarios, there is a need for enhanced activation mechanisms to bring both proteins into their fully activated conformations.

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For talin, the exertion of force on the molecule initiates the stretching of helical bundles that uncover cryptic-binding sites and thereby facilitate activation (del Rio et al. 2009; Yao et al. 2016). The force for this extension derives from the actomyosin engine, probably by binding to the third of three actin-binding sites (ABS3) at talin’s C-terminus. A modification of this site, but not of the pivotal ABS2 to decrease the binding of actin, can significantly impede the formation of adhesions (Atherton et al. 2015a, b). Coexpression of active vinculin that binds to talin at the vinculin-binding sites (VBS) and fosters ABS2 attachment to the actin cytoskeleton can, however, repair this deficiency (Atherton et al. 2015a, b). For this reason, force is possibly not the only factor that enables complete activation of talin. Kank2, a component of adhesions newly identified, binds directly to talin, activates talin and enhances the binding of talin to integrins. Curiously, this interplay decreases talin–actin binding in ABS2 and encourages adhesion (Sun et al. 2016). Talin’s ability to perform stretching operations is crucial for its function as a mechanosensory protein. Talin is capable of altering the accessibility of binding sites for interacting focal adhesion proteins in reaction to shifts in the forces induced by actomyosin interactions. The stretching of talin in a step-driven manner due to the stretching of each individual helical bundle (Yao et al. 2016) evokes the recruitment of vinculin to focal adhesions (Yao et al. 2014a, b). The open-shaped and hence activated vinculin facilitates the further targeting of multiple other focal adhesions proteins (Carisey et al. 2013), such as a large number of signaling proteins which impact the Rho and Rac GTPase signal transduction pathways (Carisey and Ballestrem 2011; Atherton et al. 2015a, b). It has been demonstrated that purely, forces can lead to an activation of vinculin (Carisey et al. 2013; Case et al. 2015) and vinculin are subject of forces in the focal adhesions of living cells (Case et al. 2015; Grashoff et al. 2010). However, vinculin’s activation state in focal adhesions seem to be either active or inactive, since no intermediate states have been found so far, which is in contrast to talin that can exhibited rather different stretched states and therefore may exhibit intermediate activation states (Margadant et al. 2011). One way in which vinculin can guide mechanosensors is by adjusting its cyclic turnover to and from the adhesion complex. In fact, focal adhesions are reported to be vastly dynamic structures. In the same way, the adhesion proteins themselves are subject to a fast transition into and out of the adhesion site (Stutchbury et al. 2017). As part of “molecular clutch,” both talin and vinculin experience forces in living cells demonstrably through the employment of FRET-based tension sensors (Austen et al. 2015; Grashoff et al. 2010; Kumar et al. 2016). Through contact with actin, these two proteins are probably triggered by the small forces to retard the retrograde flow (Chen 2005). Photokinetic studies of talin and vinculin show that their conversion is diminished once they are active (Humphries et al. 2007; Atherton et al. 2015a; Kumar et al. 2016). The active talin–vinculin complex keeps integrins in an activated conformational state (Carisey et al. 2013). As a result of the orderly recruitment and liberation of other focal adhesion signal proteins such as FAK and Paxillin, such a mechanosensory core unit seems to initiate downstream signal phenomena.

4.6.2.5 FAK and Paxillin Fulfill Key Functions in Mechanosignaling An increase in the tyrosine phosphorylation of FAK and paxillin as the first response to several mechanical stimuli on the surface has been identified (Mitra et al. 2005). Both FAK and paxillin exhibit a relatively high tyrosine phosphorylation based on the interaction of FAK with Src and on possibly Abl kinases (Gotoh et al. 1995). However, the tyrosine phosphorylation is regarded to be crucial for the process of mechanosensing (Fig. 4.3). For instance, the inhibition of FAK and Src signaling impairs the reaction of the cells toward a stretch in a cyclic application (Wang et al. 2001a, b, 2005; Sai et al. 1999). Phosphorylation of FAK at Y397 proved to be higher on stiff substrates than on soft ones (Friedland et al. 2009; Bae et al. 2014), and the phosphorylation of paxillin after the activation of myosin II has been shown to facilitate the localization of the adapter protein vinculin

4.6 Mechanotransduction

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Fig. 4.3 Structure of focal adhesion kinase (FAK) and the FAK interacting molecules

toward focal adhesions (Case et al. 2015; Pasapera et al. 2010). The crucial role of the phosphoprotein FAK has been revealed by analyzing FAK-null cells that were not able to perform durotaxis that is usually the cell migration in the direction to a stiffer substrate (Wang et al. 2001a, b). An interesting aspect is that FAK isoforms can be phosphorylated more rapidly (Toutant et al. 2002). Hence, it can be hypothesized that the activation of FAK in a faster manner can serve as are regulatory cue that cells employ to pre-trigger themselves for the softer stiffness phenotype. This seem to be of high interest in relatively soft environments (  1 kPa) that specific cell types, such as neurons, experience and which are known to contain various FAK isoforms compared to other cells of the connective tissue, such as fibroblasts (Armendariz et al. 2014). In contrast, the treatment of cells with Src and FAK inhibitors which abolished the tyrosine phosphorylation completely with in focal adhesions revealed that focal adhesions are formed in a normal way and contain the same composition of focal adhesion proteins in control cells displaying normal phosphorylation levels (Horton et al. 2016). Although the impairment of the phosphorylation did not impact the maturation of focal adhesions or the transmission of traction force toward elastic substrates, the migration of cells is fully inhibited (Stutchbury et al. 2017; Horton et al. 2016). To investigate the turnover of vinculin, we used vinculin constructs tagged with photoactivatable-GFP and assessed fluorescence loss after photoactivation (FLAP). These FLAP experiments determine the turnover of vinculin under specific conditions and indicate that the mechanosensing, which is the identification of slower turnover of vinculin on stiffer substrates, was still functional, whereas this mechanical sensing was not translated efficiently to a response by the cell (Stutchbury et al. 2017). The exposure of cells to specific frequency of ultrasound has been employed in clinical trials to support healing processes, and on the protein level, it impairs the turnover of vinculin in focal adhesions, although the signaling of FAK is impaired (Atherton et al. 2017). Downstream of the mechanosensing action of vinculin, FAK fulfills a crucial and necessary function in providing a cellular reaction (Atherton et al. 2017). After these observations, a model is offered that incorporates the molecular clutching and dynamics of focal adhesion proteins and how this interferes with adhesion signaling. Talin and vinculin deal with the retrograde flow of actin and thereby adapt their turnover rate and activation

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state. In turn, the recruitment of other focal adhesion-associated signaling proteins is facilitated. Through the regulation of the molecular stoichiometry of adhesions, talin and vinculin therefore operate as major regulators of the adhesive signaling, although they cannot control the generation of the signaling process themselves. The subsequent signaling through focal adhesion proteins such as FAK and paxillin on the GTPases Rho and Rac (Mitra et al. 2005) governs contraction which leads to increased growth of the adhesion site (Ridley and Hall 1992) or the polymerization of actin at the cell’s anterior front, which induces the formation of new focal adhesions. Consequently, a complicated feedback loop system is established, in which adhesion reacts to and impact the entire actin cytoskeleton. Within this model, adhesion proteins are able to work together by sensing the mechanical cues (mechanosensing) and by reacting to mechanical alterations through providing signaling processes throughout the cell (mechanosignaling). Finally, it can be concluded that the combination of both elements, the mechanosensing and mechanosignaling events, is necessary for the proper mechanotransduction cascade.

4.6.2.6 Other Mechanotransduction Components The adhesion proteins described so far are not exclusively involved in the mechanotransduction processes. In fact, a subset of LIM-domain containing proteins, including paxillin, Hic-5 and zyxin, performs essential tasks in the regulation of a cellular reaction upon stimulation with mechanical forces and subsequently, also in the facilitation of the downstream signal transduction processes that may additionally cause alterations in the transcription of distinct genes (Schiller et al. 2011; Smith et al. 2014; Kadrmas and Beckerle 2004). Moreover, integrin-associated complexes consist of multiple actin-regulating proteins that are able to drive the polymerization and/or the bundling of actin. These proteins have also been revealed to fulfill crucial roles in the process of mechanotransduction. For instance, the a-actinin associates with actin (Youssoufian et al. 1990), integrins (Otey et al. 1990) and vinculin (McGregor et al. 1994) and hence is supposed to facilitate the transmission of forces and the maturation of the integrin-associated complex (Roca-Cusachs et al. 2013), which points out to a critical role in mechanotransduction. Multiple other proteins that can bind to actin are located inside integrin-associated complexes, which encompass VASP, vinexin, ponsin, Arp2/3 and ArgBP2, and all of them can interact with vinculin’s neck region serving as a mechanosensory protein (Carisey and Ballestrem 2011). Besides this interaction, these actin interacting proteins are capable to reorganize actin. Nonetheless, vinexin seems to be crucial for the sensing of the rigidity in mouse embryonic fibroblasts (Yamashita et al. 2014). Apart from vinculin, the functional roles of VASP and Arp2/3 are less well known; however, they contribute locally to the precise regulation of the polymerization of actin at the focal adhesion sites (Chorev et al. 2014). Arp2/3 has been shown to interact and hence colocalizes with FAK (Serrels et al. 2007) and vinculin (DeMali et al. 2002) within integrin-associated complexes at the cell’s anterior edge; however, Arp2/3 fulfills a functional role in directing both vinculin and FAK toward actin-rich lamellipodia, where they need to initiate the formation and assembly of new adhesions.

4.7

Integrin-Associated Complex-Facilitated Mechanotransduction Causes Transcription and Differentiation

The extracellular matrix features can affect the motility of cells and the expression of distinct genes in an integrin-associated complex-based manner. Traction force experiments performed on polyacrylamide gels revealed a pronounced cellular response to stiffness alterations in terms of the expression of distinct genes that regulate finally the differentiation of cells. For instance, mesenchymal stem cells (MSCs), which adhered to soft substrates of approximately 1 kPa, start to differentiate toward a

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neuronal lineage, whereas MSCs adhered to more rigid substrates of approximately 10 kPa differentiate to a myoblast or on even stiffer substrates of approximately 30 kPa to osteoid phenotypes (Engler et al. 2006). In addition, this behavior can be impaired by the treatment of cells with the myosin inhibitor blebbistatin, which suggests that the intracellular actomyosin-dependent tension is required for the mechanosensitivity feature of cells. The plasticity in the differentiation of cells due to the mechanical characteristics of the polyacrylamide gels, such as various stiffnesses, is evoked by the signal transduction to the basically two transcriptional co-activators, the Yes-associated protein (YAP) and the transcriptional co-activator with PDZ-binding motif (TAZ). These YAP/TAZ co-activators can shuttle between the nucleus and the cytoplasm due to stiffness alterations of the cell’s microenvironment. For instance, a stiff substrate induces the localization of YAZ/TAZ in the nucleus, which is associated with higher transcriptional activity (Dupont et al. 2011). Another important finding is that the stiffness-based differentiation of MSCs into either adipocytes, when placed on a soft substrate, or osteoblasts, when placed on a stiff substrate can be fully abolished through the knockdown of vinculin that even causes a perturbed nuclear localization of the YAP/TAZ proteins (Kuroda et al. 2017). Mechanosensing by talin seems to determine the localization and the activity of YAP/TAZ on 2D microenvironments. Fibroblasts lacking both isoforms of talin such as talin1 and talin2 cannot react the changes in the stiffness of their underlying substrate through the translocation of YAP in the nucleus (Elosegui-Artola et al. 2016). Moreover, there seems to be a force threshold for the unfolding of talin, which thereby enables a mechanoresponse. When four threonine residues within the mechanosensitive R2R3 domain of talin are mutated, the hydrophobic region is narrowed (Goult et al. 2013), the threshold for the force-based unfolding of talin is shifted, and also, the induction of the nuclear targeting of YAP is elevated. In another experimental approach, the expression of a vinculin fragment (referred to as vinD1) which carries only the talin-binding region without any other functional domains similarly impaired the stiffness-induced nuclear translocation of YAP. Hence, this finding leads to the hypothesis that talin and vinculin act together to perform the (Elosegui-Artola et al. 2016). YAP/TAZ proteins provide a phenomenon that is referred to as a “mechanomemory effect,” where the cells maintain to react to the increased stiffness levels, although they have meanwhile been reduced. For instance, MSCs and myofibroblasts exhibited elevated fibrotic activity, which is associated by increased a-SMA expression, when the cells are seeded and cultured on rather stiff surfaces. A special feature is that the cells continue to possess increased higher fibrotic activity even when they are placed on substrates with lower stiffness, which resembles a healthy microenvironment. Thus, the cells maintain their activity after they have experienced the stiff microenvironment for a specific time frame (Li et al. 2017; Balestrini et al. 2012; Yang et al. 2014a, b). There is a lot of evidence that there exists a deep cross talk between integrin-associated complexes and the nucleus that itself acts a mechanosensory compartment or organelle (Cho et al. 2017). Although a lot of insights have been revealed about the involved molecules facilitating this event, still many questions remain unanswered. Hence, it is still not precisely clear which proteins are triggering the transmission of the mechanosignal from an integrin-associated complex toward the nucleus after the mechanosignal has been sensed the two focal adhesion molecules vinculin and talin. It seems to be possible that there is also a cross talk between other mechanosensitive cellular proteins or structures, such as those at cell–cell adherence junctions. It needs to be still to be resolved how these proteins or structures interconnect to precisely provide the mechanical signaling. The exact mechanism through which the cells detect and transfer the mechanical signal is a complicated event that associates various proteins at several cellular compartments. Within the cell– extracellular matrix adhesions, the process of mechanotransduction seems to be further divided into specific modules. The mechanosensing unit encompasses talin, vinculin and possible additional other

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distinct proteins that directly connect integrins to actin. Besides FAK and paxillin, other mechanosignaling proteins, such as the actin polymerization factors including Arp2/3 and VASP or actin-cross-linking proteins including a-actinin, all of which can interact with actin to drive the mechanotransduction processes that regulate vastly distinct physiological functions in living cells. There are still numerous open questions that are still not yet answered completely and which are not entirely new; instead, they need be gained new information from 3D microenvironments or the in vivo situation in living tissues in order to fully answer them. In a 3D microenvironment, a major difficulty is to develop an experimental setup, in which these complex parameters can be assed and monitored easily and precisely. In part, the difficult is based on the complexity of the extracellular matrix meshworks, such as purified systems including fibrin or collagen fiber networks that seem to be highly heterogenous. Nonetheless, the increasing information about the chemical and physical nature of the extracellular matrix scaffolds, and how these parameters impact the behavior of the cells, will finally contribute to a much clearer picture of how cells can adapt to varying conditions in vivo. A different approach may be to work with synthetic biomaterials, in which the mechanical features of the fibers can be distinguished from the features of the overall network structure in order to decipher the individual and independent contribution of the nature of the fibers and the overall network. During the last decade, biophysical techniques such as mass spectrometry and super-resolution microscopy have gained information of the precise composition of the protein network during mechanotransduction events. Although many advances have been reported of those networks, there exists that still major blind spot in our understanding of the wiring of the networks is regulated. The combination of the in vitro biochemical structural protein analysis connected with the in cellulo and in vivo employment of biosensors, which rely on the FRET technique (Grashoff et al. 2010; Brenner et al. 2016), and analysis of protein dynamics will heavily contribute to the identification of the activation mechanisms that initiate process of mechanotransduction. Biotechnological improvements, such as the discovery of the CRISPR technique, have initiated novel in vivo approaches. The usage of conditional knock-outs and knock-ins with mutant proteins in a wide range of different model organisms will help to reveal the impact of specific domains within a distinct protein on the alteration of the mechanical properties of tissues and how these tissue mechanics changes can be detected in healthy and disease conditions. Model organisms including Drosophila have helped to identify partly the in vivo relevance in mechanotransduction processes (Hakonardottir et al. 2015; Klapholz et al. 2015). Before the discovery of the CRISPR technique, the model organisms need to be quite simple, but with this important technique, the model systems can be even mammalian species. However, there is still much work needed to reveal the overall complex mechanotransduction in 3D microenvironments that seem to be definitely required to precisely investigate this mechanotransduction event. At the same time, the structural analysis of the mecahnotransduction event seems to be similarly required in vitro. In fact, the combination of in vitro analysis of individual proteins or their domains with the cellular analysis in 3D model systems will improve the knowledge of how mechanotransduction is performed in vivo and how they become malfunction in diseases, such as cancer.

4.8

Force Sensors Serve as Mechanical Markers

The precise quantification of traction force or stress transmitted by the cells in native tissues necessitates the knowledge of the tissue’s material properties and hence deserves a biophysical technique for tracking the deformations induced by the cells in real time. Hence, the 3D traction force microscopy has been employed to determine the mechanical stresses, including the convergence and

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expansion process, that are transmitted during the gastrulation step in Xenopus laevis (Zhou et al. 2015). In fact, the traction stresses can be explored at the level of tissues. In order to reveal a precise spatial resolution, oil droplets can be transferred into tissues that have a diameter of a few µm (Campàs et al. 2014; Lucio et al. 2015). The surface of those oil droplets can be covered with specific ligands for cell surface receptors, which enables cells to exert forces on the droplets. The deformation of a droplet leads to shape alterations. The material properties of the oil droplet available, the deformation of the oil droplet, which causes by the nearby cells, can be converted into mechanical stresses. Initial experiments have been performed in embryos of Drosophila melanogaster, but can be easily transferred to other tissues and organisms. Forces play a role during the physiological development of tissues in embryos, tissue repair upon injury, immunological reactions involving the migration of immune cells or during pathological conditions, such as cancer. In order to measure forces of cells, a stretch sensitive fluorescence resonance energy transfer (FRET) can be used, which offers the inherent capability to perform noninvasive tension measurements in living tissue. A FRET-based actinin tension sensor was inserted into Xenopus laevis embryos, and this sensor detected tension alterations during differentiating ectoderm. The actinin tension sensor, which comprises a spider silk protein that connects mCherry and EGFP, has been tested in human embryonic kidney cells (HEK) and embryos. In fact, this specific tension sensor colocalizes with actin filaments, and the FRET signal is altered in this amount due to the remodeling of actin filaments, the inactivation of myosin or the perturbation by osmotic pressure. A time-lapse FRET analysis revealed that the future neuronal ectoderm carries a higher tension during gastrulation and neurulation than the epidermal ectoderm, and that, the morphogenetic characteristics of the cells are correlated with the tension alterations (Yamashita et al. 2016). Several biophysical techniques are available to measure physical properties of single cells with atomic force microscopy or atomic force microscopy (cellular stiffness) or micropipette aspiration (membrane tension). These two biophysical techniques are none invasive. A disadvantage of these two is that the direction of forces cannot be determined, and it hence cannot be addressed. In order to investigate the intercellular tension in an embryo, laser ablation, hole punching (Varner et al. 2010) and the calculation from cellular geometry (Ishihara and Sugimura 2012) seem to be suitable (Yamashita et al. 2016). Moreover, the measurement of intracellular forces is much more complicated and requires the development of appropriate force sensors that can be transfected transiently or stably in living cells.

4.8.1 Force Sensor One option is to construct a molecular force sensor that can detect in real time the traction voltages transmitted by living cells in their vicinity. Such an approach potentially relies on the identification of intracellular proteins that stretch in response to an applied force, the amount of stretch resulting in an increase or decrease in Förster Resonance Energy Transfer (FRET) of fluorescent peptides attached to the protein (Grashoff et al. 2010; Borghi et al. 2012; Cai et al. 2013; Yamashita et al. 2016). The majority of FRET-based cellular force sensors are based on the knowledge of the organization of focal adhesion and adherence junctions and hence utilize focal adhesion proteins, such as vinculin, talin, aactinin, paxillin and E-cadherin, to quantitative measure the forces that are exerted them within the cytoplasm. Hence, the knowledge on how the actomyosin machinery functions at specific points creating cell–matrix or cell–cell adhesions can be gained via these sensors, and even, the usage of distinct force sensors in living embryos or even in adult animals has been reported (Kelley et al. 2015).

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FRET-based tension sensors represent a non-invasive technique to determine the forces of cells in in vitro cell culture systems (Grashoff et al. 2010; Borghi et al. 2012; Cai et al. 2013; Yamashita et al. 2016). In specific detail, the FRET-based tension sensors are compromising a cell adhesion molecule, in which a FRET domain is incorporated that includes two fluorescent domains linked by an elastic element. Among these cell adhesion molecules are vinculin (Grashoff et al. 2010), a-actinin (Borghi et al. 2012), b-spectrin (Krieg et al. 2014) or even the prominent cell–cell adhesion receptor Ecadherin (Cai et al. 2013; Yamashita et al. 2016). When the sensor experiences tension, the elastic part of the sensor is stretched, and thereby, the distance of the fluorescent domains increases, which subsequently diminishes the efficiency of the FRET system. In fact, these tension sensors deliver the forces within a cell at a subcellular resolution; however, they are rarely used in living tissues (Krieg et al. 2014; Kelley et al. 2015) and an adapted tension sensor for in situ force measurements is required.

4.8.2 Besides Vinculin, Talin Acts as a Force Sensor Integrin-dependent cell adhesions are identified as mechanosensitive elements, where talin facilitates the connection of actin filaments to integrins either directly or indirectly by the recruitment of vinculin. Hence, it seems to be suitable to develop a force sensor based on talin. It has been reported that talin is under tension, when it is located in focal adhesions. Moreover, the tension of talin is increased, when it is present in peripheral than in central focal adhesions of the cells. Vinculin manages to enhance the tension on talin, which relies basically on the actin-binding site 2 (ABS2) that is located in the center of the rod domain, and rather not on the ABS3 the outermost regions of the Cterminus. Dissimilar to vinculin, talin is less tensioned when the cells experience rather soft substrates. The central and peripheral focal adhesions of cells are distinguishable, since the central one needs the ABS3 region of vinculin to interact with actin, whereas the peripheral ones require ABS2. In contrast to vinculin, talin utilizes the ABS region in vinculin for the sensing of stiffness cues and not the ABS3 region (Kumar et al. 2016). These results lead to the hypothesis that the central and the peripheral focal adhesions need to highly organized and regulated in different manners as well as that the two domains of ABS2 and ABS3 can adapt their functions to spatial alterations and the sensing of the stiffness. Finally, these results have elucidated the function of talin and triggered the development of constraining models for the process of cellular mechanosensing. Integrins couple the extracellular matrix to the actin cytoskeleton by involving a complex assembly of connections in which the focal adhesion protein talin fulfills a crucial task (Ziegler et al. 2008; Calderwood et al. 2013). The head domain (synonymously termed N-terminal FERM domain) of talin is located in its N-terminal part and can directly interact with the cytoplasmic part of the integrin b subunit and is also needed for conformation-based activation of integrins that can then interact with extracellular matrix proteins in a high affinity manner. In specificity, talin has three Factin-binding sites (ABSs). Among the ABS, the far C-terminal-binding site within the rod domain, the ABS3, seems to be the key sites. The rod domain of talin possesses several vinculin-binding sites that are somehow hidden in four and five a-helical bundles. Under mechanical stimulation, the domains of talin uncover the binding regions for the head domain of vinculin that in turn causes a reinforcement of connections to actin by the ABS region within the tail domain of vinculin. The lack of talin in selected organisms causes distinct phenotypes, which closely resemble the phenotypes caused by deletion or mutation of the integrins on the cell’s surface indicating key function of talin (Monkley et al. 2000; Brown et al. 2002; Cram et al. 2003). Due to mechanical alterations in the microenvironment, tissues can adapt their mechanosensitivity through integrin-based focal adhesions by altering their function and the expression of specific genes

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(Orr et al. 2006; Costa et al. 2012). For instance, cells have the capacity to detect the mechanical stiffness of the surrounding environment, such as the extracellular matrix scaffold, and can accordingly adapt their cellular contractility, the mechanosignaling, and the expression of genes, which is commonly known as stiffness-sensing behavior (Humphrey et al. 2014). These effects encompass the alteration of the production of extracellular matrix through changes in the stiffness of the matrix and forces exerted externally. The mechanosensing process facilitated by integrins is essential for the development and multiple diseases, such as fibrosis, cancer and hypertension (Orr et al. 2006; Butcher et al. 2009). The force transmission is based on highly dynamics couplings between integrins and actin, whereby F-actin can flow over the focal adhesions, when a force is exerted by the polymerization of actin and the filament sliding induced by myosin (Case and Waterman 2015). At focal adhesions close to the cell borders, the actin flows backward over the rather non-moving integrins, whereas talin and vinculin can move also backward with an intermediate velocity. Hence, the connections between the integrins and the F-actin fibers need to be dynamically arranged, which in turn requires that the adaptor proteins vinculin and talin need to be highly dynamic, since they need to quickly be associated or dissociated upon the transmission of forces to drive the transmission of forces that is known as a focal adhesion clutch. There is still a major question to be raised of how this dynamic assembly facilitates the process of mediates mechanotransduction. The establishment of a biophysical technique to determine the forces over a distinct molecule, such as the FRET-based approach, where two fluorophores are coupled to a calibrated spring region, revealed that vinculin is directly subject to mechanical tension inside focal adhesions (Grashoff et al. 2010). Additionally, a talin tension sensor has been invented that revealed the functional role of talin during the mechanical force exertion at integrin-facilitated focal adhesion and the process of mechanotransduction. FRET-based tension sensor modules can be utilized to determine how specific proteins deal with mechanical forces in living cells. Nevertheless, the forces in the single-pikonewton (pN) regime are still cumbersome to solve, and tools for multiplexed tension measurement are unavailable. A genetically coded, FRET-based biosensor, termed FRET-based tension sensor modules, which is characterized by an almost digital force behavior and an elevated sensitivity at 3–5 pN, is generated and calibrated (Ringer et al. 2017). Moreover, a technique is reported that enables the simultaneous analysis of coexpressed tension sensor modules based on two-color fluorescence lifetime microscopy. Finally, a procedure for calculating the proportion of mechanically linked molecules in cells is presented. The transfer of these techniques to new talin biosensors revealed that there is an intramolecular tension gradient over talin-1 due to integrin-driven cell adhesion (Ringer et al. 2017). In specific detail, the tension gradient is based on the actomyosin machinery and on the linkage through vinculin and can in a sensitive manner detect the rigidity of the extracellular matrix microenvironment. The cells have the capacity to adhere and sense alterations in the stiffness of tissue that is essential for the proper development of organs and their function. There is still a lot to explore, since the key mechanisms through which adherent cells feel the compliance of the extracellular matrix scaffold are not clearly understood. With two single-molecule calibrated biosensors, which permit the analysis of a hitherto unattainable, but physiologically extremely important force regime in cells, it is demonstrated that the integrin activator talin produces mechanical connections during cell adhesion, which are indispensable for cells to examine tissue stiffness. Talin connections can be subject to various piconewton (pN) forces, but usually, they carry about 7–10 pN during the cell adhesion, which relies on the coupling strength to F-actin and vinculin (Austen et al. 2015). The break of the mechanical coupling of talin leads inevitably to the activation of integrins and the initial cell adhesion, although it avoids the strengthening of focal adhesion and thus the acquisition of extracellular stiffness. The talin

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mechanics are isoform-dependent, and therefore, the expression of either talin-1 or talin-2 influences the sensing of the extracellular stiffness. The rigidity of tissues represents an epigenetic factor guiding the patterning of tissues and the proper development of organs (Heisenberg and Bellaiche 2013; Wozniak and Chen 2009; Franze et al. 2013), whereas alterations in tissue mechanics seem to be connected to various disease states, such as development of cancer, cardiovascular disorders or injury of the spinal cord (Ingber 2003; Lu et al. 2011). To discriminate between tissue stiffness variations, the cells continuously evaluate the mechanical properties of their environment by binding and contracting the surrounding extracellular matrix (Discher et al. 2005; Plotnikov et al. 2012; Schoen et al. 2013). This attachment-dependent rigidity analysis is facilitated by focal adhesions, subcellular structures in which extracellular cell– matrix receptors, such as integrins, are linked to the cell’s actin cytoskeleton through adapter proteins (Geiger et al. 2009; Hoffman et al. 2011; Elosegui-Artola et al. 2014). Although the essential role of single integrin subunits and various focal adhesion molecules, such as focal adhesion kinase (FAK), vinculin and paxillin, has been recognized (Plotnikov et al. 2012; Elosegui-Artola et al. 2014; Grashoff et al. 2010), the key mechanism linking the cell adhesion to mechanosensing activity is still unclear. One of the involved regulators of focal adhesion mechanosensors is talins, which are mainly characterized by their fundamental role in integrin activation (Critchley 2009). Talins connect and activate integrin receptors directly with an N-terminal head domain and are supposed to transmit mechanical signals by linking to the actin cytoskeleton at the same time with their C-terminal rod domain (Kanchanawong et al. 2010; del Rio et al. 2009; Liu et al. 2015). However, in the absence of suitable techniques for measuring subcellular talin forces, there was no quantitative proof of the mechanical tension via the talin in the cells. For this reason, biosensors have been developed to study the piconewton (pN) mechanics of talin compounds in living cells.

4.9

The Lamellipodium Acts as an Alternative Force Sensor

The capacity of adherent cells to perceive alterations in the mechanical properties of their extracellular environment is crucial for many aspects of their physiology. There exists evidence that cell adhesion and spreading are both sensitive to the rigidity of the substrate. It has been shown that this behavior is biphasic, since a transition point is approximately at a Young’s modulus of 7 kPa. In contrast to established hypotheses, it has been shown that this behavior is independent of the activity of myosin II. Instead, the cell spreading on soft substrate seems to be impaired through the decreased nascent adhesion formation in the absence of myosin II throughout the lamellipodium. Cells cultured on soft substrates exhibit a normal activity of their leading-edge protrusions; however, these protrusions are not further stabilized, since the ongoing assembly of the adhesions is blocked. Increasing the integrin–extracellular matrix affinity by the addition of Mn2+ ions can induce the assembly of nascent adhesions and spreading of cells on soft substrates. Employing a computational model for the simulation of the assembly of nascent adhesions, the biophysical properties of the integrin–extracellular matrix have been identified to strengthen the binding interactions over a distinct matrix stiffness threshold, which is in line with experimental results (Oakes et al. 2018). Based on these findings, it can be proposed that myosin II-independent forces inside the lamellipodium facilitate the mechanosensation process that subsequently supports the formation of new adhesions, which then regulate the proper cell spreading. Moreover, this proposed mechanism for myosin II-independent sensing of the substrate stiffness may facilitate a variety of several stiffness-sensitive processes. The capacity of cells to feel the mechanical forces and switch them into biochemical signals drives a wide variety of physiological functions (Paluch et al. 2015; Iskratsch et al. 2014; Charras and Sahai

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2014). In specific detail, the cells react to alteration in the extracellular matrix stiffness through the adaption of several adhesion-dependent behaviors, including the processes of cell spreading (Lo et al. 2000; Yeung et al. 2005; Prager-Khoutorsky et al. 2011; Oakes et al. 2009; Califano and ReinhartKing 2010; Han et al. 2012; Ghibaudo et al. 2008; Tee et al. 2011; Engler et al. 2004), proliferation (Wang et al. 2000), differentiation (Engler et al. 2006; Fu et al. 2010), cell migration (Lo et al. 2000; Breckenridge et al. 2014; Raab et al. 2012), and metastasis (Paszek et al. 2005; Mekhdjian et al. 2017). Matrix mechanosensing process seems to be driven basically by focal adhesions, which are hierarchical organized organelles that include approximately over 150 proteins providing the dynamic and force-sensitive interplay between actin cytoskeleton and the extracellular matrix scaffold (ZaidelBar et al. 2007; Schiller et al. 2011; Kanchanawong et al. 2010). These dynamical remodeling organelles facilitate the mechanical sensing of their local environment in a wide range of physiological conditions, and their detailed function is still not clearly revealed. Many studies addressed myosin II-driven mechanisms for the sensing of the substrate stiffness (Chan and Odde 2008; Macdonald et al. 2008; Bangasser and Odde 2013; Elosegui-Artola et al. 2014, 2016; Plotnikov et al. 2012). The stresses, which are produced by myosin motors and act on the actin cytoskeleton, are transduced through focal adhesion toward the surrounding extracellular matrix environment. In fact, these stresses associated with matrix stiffness influence the deformation and affinity of proteins to bind within focus adhesion (Yan et al. 2015; Thievessen et al. 2013; Sawada et al. 2006; Oakes et al. 2014). Alterations in the composition of focal adhesions and the kinetics of proteins therein seem to regulate the transfer of forces originating from the actin cytoskeleton toward the surrounding extracellular matrix (Hu et al. 2007; Aratyn-Schaus and Gardel 2010; Gardel et al. 2008), which initiates the handling of focal adhesions as molecular clutches. The initial assembly of an adhesion takes place at the cell’s leading edge, which is the outermost region of the lamellipodium and represents a myosin-independent process (Bachir et al. 2014; Alexandrova et al. 2008). The emerging structures are termed nascent adhesions and are themselves stimulated by forces, which are produced by the actin filament polymerization. However, the role of nascent adhesions for the sensing of the stiffness of the substrate has not yet been determined precisely. Ligand-coated substrates represent one of the best-characterized metrics for the microenvironmental sensing through the attachment and spreading of adhesive cells. The density and spatial organization of matrix ligands determines the degree of cell spreading (Cavalcanti-Adam et al. 2007; Reinhart-King et al. 2005; Dubin-Thaler et al. 2004) together with overall the rigidity of the substrate that resembles the ligands on the surface (Lo et al. 2000; Yeung et al. 2005; Prager-Khoutorsky et al. 2011; Oakes et al. 2009; Califano and Reinhart-King 2010; Han et al. 2012; Ghibaudo et al. 2008; Tee et al. 2011; Engler et al. 2004). Moreover, it has been hypothesized that the stress-relaxing features of the matrix promote the spreading of cells (Chaudhuri et al. 2015; Bauer et al. 2017). When the softness of the substrates lies below a Young’s modulus of 500 Pa, the spreading of the cells impaired. When the stiffness of the substrate increases, the spreading area is accordingly increased, and finally, it reaches a plateau (Califano and Reinhart-King 2010; Han et al. 2012; Ghibaudo et al. 2008; Tee et al. 2011; Engler et al. 2004). Based on slight variations in the experimental approach, differences in the exact stiffness range controlling this behavior can be observed (Denisin and Pruitt 2016); however, cell spreading still is a robust method to analyze the sensing of substrate stiffness. In order to reveal the mechanism that controls the substrate stiffness-based spreading of cells, NIH 3T3 fibroblasts were analyzed. Indeed, the spreading of NIH 3T3 fibroblasts is precisely altered by the Young’s modulus of the substrate that is elevated from 5 to 8 kPa. In specific detail, cells cannot sufficiently spread out fully on substrates with a weak stiffness of about 5 kPa. When the substrate stiffness increased to values above 5 kPa, the spreading area is increased until a plateau region is reached, when the substrate stiffness is higher than 8 kPa. The spreading area stays constant far beyond this limit. Unexpectedly, it was found that this stiffness-dependent alteration of cell spread

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was independent of the motor activity of myosin II. In contrast, on soft substrates, the spreading is compromised by the reduced accumulation of nascent, myosin-independent adhesions on the cell periphery. The improvement of the integrin ligand affinity by the addition of Mn2+ was necessary to stabilize the nascent adhesions as well as to enlarge the cell spreading area on soft substrates. Subsequently, a computational model was applied to determine how shifts in the catch-bond kinetics of the integrin influence the integrin binding on substrates of various stiffnesses. It was observed that the biophysical properties of integrin matrix catch bonds were improved to detect alterations in substrate stiffness at approximately 6 kPa, in accordance with experimental outcomes. Finally, these findings show that the assembly of nascent adhesions within the lamellipodium functions as a myosin II-independent mechanosensor to regulate the adhesion and spreading of cells.

4.9.1 The Spread Area Results from a Biphasic Response of the Stiffness of the Substrate Which Is Independent of Myosin Activity To analyze the mechanisms for the sensing of the substrate stiffness, the spreading area measurement of adhesive cells can be employed. Specifically, NIH 3T3 fibroblasts were seeded on a wide range of polyacrylamide gels with covalently bound fibronectin, a ligand for integrins, displaying Young’s moduli from 0.6 to 150 kPa. For control, the cells were seeded on glass with absorbed fibronectin. In line with the previous studies (Lo et al. 2000; Prager-Khoutorsky et al. 2011; Califano and ReinhartKing 2010; Han et al. 2012; Ghibaudo et al. 2008; Tee et al. 2011), the spreading area of the cells area correlates with the stiffness of the substrate. In contrast, it has been revealed that there are basically two regimes: Firstly, there was solely poor spreading detected on soft (below approximately 5 kPa) substrates, and secondly, a high degree of cell spreading can be seen on stiff (above approximately 8 kPa) substrates, with a well-defined transition point between these two values and no statistical difference in the spreading behavior between cell populations within each these two regime. The morphology of cells on soft and stiff substrates has been revealed to be vastly variable (Yeung et al. 2005). In specific detail, cells adhered to soft substrates display a more rounded shape, and their cytoskeleton is rather disturbed and purely organized. An opposite behavior can be observed, when cells are positioned on stiff substrates, on which the cells possess a rather polarized cell shape and their cytoskeleton appears to be rather organized by F-actin stress fibers spanning over the entire cytoplasm. Since myosin II activity is associated with mechanosensing (Plotnikov et al. 2012), it can be proposed that the impairment of myosin II has no effect on the spreading area on a wide variety of substrate stiffness values. Nevertheless, cells treated with 50 lM of the myosin ATPase inhibitor blebbistatin displayed still biphasic response due to varying substrate stiffness. After treatment with blebbistatin, the cells possess an increased spreading area than untreated controls for a wide range of substrate stiffnesses, whereas they displayed the same behavior independently of whether the experiment has been performed on soft and stiff regimes. Myosin II-inhibited cells exhibited on all substrates increased cell protrusions, whereas when placed on stiff substrates, the cells exerted increased spindle-like membrane extensions. When the Rho-kinase inhibitor Y26732 has been employed, the cells showed similar phenotypes and additionally when they were cultured on other proteins of the extracellular matrix scaffold. Finally, alterations in the spreading area of cells have not been observed between soft and stiff regimes and are independent of the myosin II activity.

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4.9.2 Dynamic Remodeling of Lamellipodia Is Not Altered by the Material Stiffness To decipher how the stiffness of the substrate affects the spreading area of cells, we analyzed how the substrate stiffness impacts on the dynamics of protrusions. The formation of lamellipodia can be analyzed by recording time-lapse images of cells that are transiently or stably transfected with a fluorescent membrane marker, such as GFP-stargazin and treated with 20 lM Y27632 on representative soft, such as 2.1 kPa, and stiff, such as 48 kPa, substrates 30 min after cell seeding. Cells located on soft substrates had repeated cycles of protrusion and retraction, which restricted their spreadability. However, cells on rigid substrates produced continuous and uniform protrusions which led to advanced progress of the leading edge. Using cell contours derived from the fluorescence pictures, we pinpointed protrusive regions and measured their morphology and properties. No statistically significant discrepancy was noted among soft and rigid substrates for average protrusion area or average protrusion width area measurements. From these observations, it appears that substrate stiffness influences the stability of leading-edge protrusions, whereas the protrusion dynamics are not concerned. The Arp2/3-induced lamellipodium generation remains necessary for propagation as cells on soft and rigid substrates, which were treated with CK869, an Arp2/3 inhibitor, could not be distinguished from control cells on soft substrates. Finally, these outcomes clarify that it is the stabilization, rather than the formation, of Arp2/3-dependent lamellipodial protrusions hampered on soft substrates. Soft substrates can inhibit the assembly of nascent adhesions. To enlighten the regulatory mechanism for the substrate stiffness-driven alterations in the stabilization of myosin II-independent cell protrusions, the assembly of these myosin II-independent nascent adhesions is investigated, which are built at the basis of the emerging lamellipodium. After two hours of cell seeding, 20 lM of the Y27632 ROCK inhibitor were added to these cells for 30 min. After fixation, the cells were triple stained for actin, the p34 subunit of the Arp2/3 complex and the adaptor protein paxillin. The p34 protein is observed at the cell periphery on both soft and stiff substrates and hence marks the Arp2/3based lamellipodium. Paxillin can assemble on stiff substrates small point-like nascent adhesions at the cell’s leading edge (Choi et al. 2008). In contrast, paxillin-enriched nascent adhesions were rarely detected on soft substrates and when present they are located far distant from the cell’s leading edge. To quantize these discrepancies in protein localization, the average actin, p34 and paxillin intensities in roughly 0.5 lm belts taken radially from the edge of the cell were evaluated. The peak of p34 intensity was located directly at the edge of the cell independently of the substrate. Paxillin was found on stiff substrates at approximately 0.5 lm of the p34 peak. On soft substrates, the accumulation of paxillin was considerably decreased, the peak of which was found approximately 5 lm beneath the leading edge. Finally, it can be summarized that cells on soft substrates display decreased nascent adhesions.

4.9.3 Mn2+-Based Integrin Activation Induces Cell Spreading on Soft Substrates Since the density of nascent adhesions on soft substrates is pronouncedly decreased, it seems to be necessary to determine whether the extent to which alterations in the integrin–ligand affinity affect the nascent adhesion assembly. The presence of 3 lM Mn2+ prolonged the lifespan of integrin–fibronectin bonds (Kong et al. 2009), but had no effect on cell contractility. When cells were deposited on soft substrates in the presence of 3 lM Mn2+ ions, they had more than a double increase in the spreading area on soft substrates, similar to their spreading area on rigid substrates either in the presence or absence of Mn2+ ions. For a direct evaluation of the effect of Mn2+ ions on adhesion

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assembly on soft substrates, immunofluorescence of paxillin and actin was conducted. Adding Mn2+ ions to cells on soft substrates encouraged the generation of paxillin-rich adhesions close to the cell periphery and the formation of lamellar actin bundles. To assess how fast Mn2+ initiated alterations in adhesion formation and cell spreading area, real-time cell imaging of EGFP paxillin and mAppleactin was conducted in cells deposited on a soft substrate during the administration of Mn2+ to the culture medium. Before the addition of Mn2+ ions, the protrusive activity on soft substrates was significant, without any alteration in surface area or cell shape. Upon addition of 3 lM Mn2+, the cell protrusions are stabilized, new focal adhesions can be seen, and the spreading area of the cells is enhanced. After one incubation hour, the medium was exchanged with a Mn2+ ions-depleted medium and the cells start directly retracking themselves to their initial spreading area. These results show that the presence of Mn2+ ions can induce the spreading on rather soft substrates. This result leads to the hypothesis that the integrin–fibronectin-binding strength enables cells to detect the stiffness of the underlying substrate facilitate their own spreading. The integrin catch-bond kinetics drive the sensing of the stiffness of the substrate. In order to reveal how the integrin–fibronectin interaction kinetics provides the sensing of the substrate stiffness, a computational model for the nascent adhesion assembly at the anterior edge of the edge is established. Similar to other approaches (Chan and Odde 2008; Macdonald et al. 2008; Bangasser and Odde 2013; Elosegui-Artola et al. 2014, 2016), this computational model included biophysical properties of the cell–matrix adhesions, the retrograde flow of actin and the rigidity of the substrate. Therein, individual integrins can serve as molecular clutches, and intermittently, transmitting force is evoked the retrograde flow of actin toward the substrate. Integrin–fibronectin bonds are demonstrated to act as catch bonds, which means that the lifetime of these catch bonds is extended under load (Kong et al. 2009). In order to examine the special features of these catch-bond kinetics that are required to sense the stiffness of the surrounding substrate in nascent adhesions, a simple model is employed. In the model, each of which integrins and ligands are depicted as single point objects. At first, a certain number of fibronectin molecules were randomly bound to a substrate to which integrins could bind. The integrins endured successive rounds of diffusion, binding and debonding along a quasi-2D surface, which mimics the ventral membrane of cells above the substrate. Integrins with a connection to actin are subjected to retrograde flow, such as within the lamellipodium (Kong et al. 2009), whereas unbound integrins can diffuse over the membrane surface (Rossier et al. 2012). When an integrin approached a free fibronectin, it generated a harmonic potential interaction imitating the bond with the stiffness dictated by substrate stiffness. The hypothesis of concomitant binding of integrin to substrate ligands and actin was justified by the requirement to generate tension on the integrin– fibronectin bond, which governed the lifetime of the bond. Maintaining a steady flow of actin, the forces on the bonds produced were proportional to the substrate stiffness. All model variables are based on experimental results (Alexandrova et al. 2008; Rossier et al. 2012; Xu et al. 2016; Burnette et al. 2011). In specific, the life-force relationships of integrin–fibronectin bonds from singlemolecule experiments of atomic force microscopy were directly included (Kong et al. 2009). In order to quantify the integrin binding amount, the average fraction of bound integrins has been determined during the simulations with each condition ranging between 10 and 300 s. Initially, by simulating the life-force ratio of the integrin–fibronectin bond as a step function, a catch-bond behavior has been determined to be required to measure substrate stiffness. The longevity of the binding was kept constant below a peak force of 30 pN and below 0 at higher forces. The variation of the amount of integrin fibronectin lifetime had no impact on the proportion of bound integrins in dependence of substrate stiffness. Thus, in the lack of a force-dependent catch-bond mechanism, the integrin-binding kinetics remained unaffected by substrate stiffness. Subsequently, one of the questions was how alterations in the biophysical properties of catch bonds led to a

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measurement of stiffness. To simulate the life-force ratio as a typical catch bond, and to adjust the maximum life for the 30-pN peak force, the unloaded lifetime requires to be maintained as a constant. Under these conditions, since the lifetime of the integrin–fibronectin bond accelerated with rising force and the force was constrained by the stiffness of the substrate, the actin flow improved the amount of bound integrins on stiff substrates. However, alterations in the maximum bond lifespan showed very little effect on the proportion of bound integrins for each tested substrate stiffness. Unexpectedly, the transition between the different systems of integrin binding occurred in the model around the Young’s module of approximately 7 kPa utilizing known biophysical parameters of integrin catch bonds that have been experimentally determined. When Mn2+ ions are available, integrin–fibronectin bonds exhibit both an increased affinity and a longer lifetime of the bond reacting to elevated stress related to wild-type conditions (Kong et al. 2009; Gailit and Ruoslahti 1988; Smith et al. 1994; Mould et al. 1995). In order to imitate these phenomena in the model, both results have been combined to increase both the unloaded lifetime and the peak force lifetime. Through the enhancement of the integrin–fibronectin bond lifetime for forces up to 30 pN, the actin flow elevates the number of integrins that are bound to soft substrates. In this situation, the proportion of bound integrins on soft substrates in the presence of Mn2+ ions was quantitatively comparable to the proportion of bound integrins on rigid substrates in the absence of Mn2+ ions. Therefore, although an enhancement of unloaded lifetime or peak force lifetime alone is not enough to capture the impact of Mn2+, its cumulative effect is sufficient to eliminate the effects of substrate stiffness on cell spreading. Moreover, this observation did not rely on the ligand number employed in this model. In summary, these results show that the stiffness measurement in the lamellipodium is determined by the catch-bond kinetics of integrin–fibronectin linkages and that the proportion of bound integrins is susceptible to the unloaded lifetime and the maximum lifespan of the catch-bond curve. The addition of Mn2+ ions causes an increased lifespan of the integrins on soft substrates, which in turn elevates the average portion of bound integrins. However, when this behavior of the integrin binding kinetics is altered, the cells can even spread out well on soft substrates. For many cellular processes, such as the viability, growth, cell migration and differentiation, the capability of cells to feel the stiffness of the surrounding extracellular environment is crucial (Paluch et al. 2015; Iskratsch et al. 2014; Charras and Sahai 2014). Fibroblasts have been seen to display a biphasic response in spreading on matrices with variable stiffnesses. For extracellular matrices, which have a Young’s modulus of less than approximately 5 kPa, the cells cannot spread out well and hence display a minimal assembly of adhesion with solely less organized actin filament structures. Above approximately 8 kPa, the fibroblasts possess a maximal spreading area and exhibit a typical adhesion assembly with a highly structured actin cytoskeleton. This stiffness transition seems to in the same range as the physiological stiffness of tissues (Engler et al. 2006; Califano and Reinhart-King 2010; Han et al. 2012; Ghibaudo et al. 2008; Tee et al. 2011; Engler et al. 2004). It has been proposed that the cell spreading area is regulated by the stiffness of the substrate and the dependence seems to be a power-law behavior (Han et al. 2012; Engler et al. 2004), or when the number of substrates is increased, the behavior can be seen as biphasic. Based on myosin II’s key role in the cellular force generations, myosin II has been hypothesized to fulfill a key role in the stiffness sensing of the underlying substrates by adhesive cells (Chan and Odde 2008; Macdonald et al. 2008; Bangasser and Odde 2013; Elosegui-Artola et al. 2014, 2016; KraningRush et al. 2011). It has been shown that a myosin-independent sensing mechanism of stiffness is regulated by the spreading area and is based on forces that are originating from the polymerization of actin in the lamellipodium. Integrins have been demonstrated to function as catch bonds, since they couple the cytoskeleton with the extracellular matrix and facilitate thereby the transmission of stress (Kong et al. 2009; Choquet et al. 1997). Moreover, the lifetime of these catch bonds depends on the

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amount of the applied physical load. When the load on theses integrin is elevated, the lifetime of the catch bonds is prolonged similarly (Kong et al. 2009). The prolonged lifetime of these catch-bonds can even induce the clustering and the formation of focal adhesions. In contrast, on soft substrates, the decreased stiffness causes decreased catch-bond lifetimes that impair the clustering and subsequently the formation of focal adhesions. Both experimental and simulation studies point to the fact that integrin-force-dependent-binding kinetics is mainly sensitive to substrates with a stiffness ranging between approximately 5 and 8 kPa. The addition of Mn2+ ions modifies the kinetics of integrin– extracellular matrix bonds as it prolongs the longevity of unloaded and peak force (Kong et al. 2009; Gailit and Ruoslahti 1988; Smith et al. 1994; Mould et al. 1995). Thereby, the amount of integrins is increased, and the average distance between integrins is reduced. Therefore, these effects induced the assembly of adhesions and facilitate the spreading of cells on soft substrates and impact the cell morphology on stiff substrates. The concept of this framework is reminiscent of the general motor clutch model (Suter et al. 1998) proposed as a mechanism for explaining mechanosensitivity (Chan and Odde 2008; Macdonald et al. 2008; Elosegui-Artola et al. 2014, 2016). As opposed to forces generated by myosin motors, the force acting through the integrin bonds is generated by actin polymerization within the lamellipodium. These polymerization forces modify the integrin extracellular matrix-binding kinetics and provide a remarkably simple and elegant mechanism for gaining an understanding of the measurement of substrate stiffness. Former work has shown that a minimum distance between the integrins is necessary for adhesion formation (Cavalcanti-Adam et al. 2007). The binding of integrins to their ligands also restricts their membrane diffusion (Rossier et al. 2012) and promotes nanoscale cluster formation (Bachir et al. 2014). When a nanoscale cluster of integrins emerges, the force needed to rupture adhesion, such as the adhesion strength, is over an order of magnitude larger than typical tensions induced in the cytoskeleton (Coyer et al. 2012). Adhesion stabilization is therefore improved by raising the density of the bound integrins, and the cell can spread. Finally, these results imply that the lamellipodium functions as a myosin-independent mechanosensor and exerts force on bound integrins through an actin polymerization-based retrograde flow. On soft substrates, the enhanced flexibility of the matrix results in less load on the integrin extracellular matrix binding, leading to a reduced lifetime. The shorter lifetime eliminates integrin clustering and prevents adhesion stabilization, resulting in poor spreadability. On stiff substrates, the integrin–extracellular matrix bonds can undergo a higher load and thus possess a longer lifetime, which supports the stabilization of the adhesion and thereby allows the cells to spread. Although these findings do not rule out the feasibility that myosin-generated forces can be a mechanism to examine the stiffness of the substrate, the results indicate that stiffness sensorics are obtained passively through the characteristics of integrin-binding kinetics. Since a simple shift of this kinetics is able to trigger the spreading on soft substrates, in the future it becomes useful to examine whether this concept is adequate to restore other functions compromised by soft substrates such as development, differentiation and disease. The physiology of cells may be based on the regulation of the mechanical properties of the extracellular microenvironment. The spreading of cells is basically a mechanosensitive process that is driven by weak forces that are provided by the cell’s periphery and are not based on the activity of motor proteins. Finally, it has been demonstrated that the sensing of the environmental stiffness relies on the kinetics of the initial formation of adhesion bonds, which are probed by forces based on the polymerization of proteins. On the basis of this analysis, the binding kinetics of adhesion molecules becomes sensitive to a wide variety of forces that permit mechanosensation.

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Chu et al., alpha-Catenin and vinculin cooperate to promote high E-cadherin-based adhesion strength. J. Biol. Chem. 288(7), 4957–4969 (2013) P.M. Thompson, C.E. Tolbert, K. Shen et al., Identification of an actin binding surface on vinculin that mediates mechanical cell and focal adhesion properties. Structure 22(5), 697–706 (2014) P.M. Thompson, S. Ramachandran, L.B. Case, C.E. Tolbert, A. Tandon, M. Pershad, N.V. Dokholyan, C.M. Waterman, S.L. Campbell, A structural model for vinculin insertion into PIP2-containing membranes and the effect of insertion on vinculin activation and localization. Structure 25(2), 264–275 (2017) C.E. Tolbert, K. Burridge, S.L. Campbell, Vinculin regulation of F-actin bundle formation: what does it mean for the cell? Cell Adh. Migr. 7(2), 219–225 (2013) C.E. Tolbert, P.M. Thompson, R. Superfine, K. Burridge, S.L. Campbell, Phosphorylation at Y1065 in vinculin mediates actin bundling, cell spreading, and mechanical responses to force. 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5

Structure and Function of the Mitochondrion

Abstract

This chapter introduces the organelle mitochondrion. The structure and function are outlined, described and related to the mechanical properties of the cell. It also explores basic structures, concepts and processes of mitochondria. In detail, it is presented how mitochondria function by fusion and fission. Besides the description of mitochondria in its static state, the dynamic state is included that plays a role in processes undergoing dynamically remodeling, such as cell migration and invasion. In summary, the description of mitochondria in this chapter forms the basis for understanding of Chap. 6 of the Cellular Mechanics and Biophysics book dealing with the mechanical properties of mitochondria.

5.1

Introduction to the Mitochondrion

Mitochondria are organelles with a rod-like morphology and supply the cells with energy by serving as a power generator. They convert oxygen and nutrients into adenosine triphosphate (ATP), which stores the chemical energy of the cell required for cell metabolism. The process is known as aerobic respiration and is the reason why some organisms (complex animals such as humans) breathe oxygen. Higher animals cannot exist without aerobic respiration. Another option of respiration is the anaerobic respiration, which is less efficient than aerobic respiration because aerobic respiration results in 15 times higher ATP levels. This high amount of ATP enables these complex animals to survive because they require large amounts of energy. In particular, the number of mitochondria in a cell is adapted to the special energy and hence metabolic requirements of the cells. A cell can have one mitochondrion or more than thousands of mitochondria. Mitochondria occur in animals, plants, fungi and protists and have been observed under a conventional light microscope. The isolation of mitochondria from cells leads to an understanding of mitochondrial function. The structure of the mitochondrion is important for its function. As for all organelles, the two specialized membranes represent the boundaries of the mitochondrion. The organelles are divided into a narrow intermembrane space and a wider internal matrix, both consisting of highly specialized proteins (Fig. 5.1).

© Springer Nature Switzerland AG 2020 C. T. Mierke, Cellular Mechanics and Biophysics, Biological and Medical Physics, Biomedical Engineering, https://doi.org/10.1007/978-3-030-58532-7_5

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Fig. 5.1 Structure and membrane compartments in the mitochondrion. The outer membrane covers the mitochondrial matrix from the cytoplasm, and the inner membrane contains several cristae, at which the mitochondrial energy is generated

5.2

Mitochondrial Shape and Function in Energy Production

Two billion years ago, mitochondria are developed evolutionary by the engulfment of an a-proteobacterium by an early eukaryotic cell precursor (Lane and Martin 2010). The mitochondria have retained the double membrane of their ancestors and the basic ATP production, while their entire shape and composition have been pronouncedly altered, and they have additionally claimed a myriad of cellular functions. Through the process of gaining new functions during evolution, most of the genomic material of the a-proteobacterium progenitor was wasted quickly or even transferred to the nuclear genome of the former host (Gabaldón and Huynen 2004). In human cells, a small, circular genome of approximately 16 kilobases is left, which is present in cells in a vast abundance of copies compared to nuclear chromosomes. The human mitochondrial genome stores genetic-coding information for 13 proteins that are central components of the mitochondrial respiratory complexes I–IV incorporated in the inner membrane. The respiratory chain, together with the Krebs’ cycle, generates an electrochemical gradient in the matrix through the joint transfer of electrons to oxygen and protons from the matrix via the inner membrane into the intermembrane space. The electrochemical gradient drives the end complex V of the chain, ATP synthase, an old rotary turbine machine that induces the synthesis of most cellular ATP. The electrochemical potential is exploited for key mitochondrial functions, such as buffering the signal ion Ca2+ through a uniporter-based uptake in the inner membrane (Baughman et al. 2011; De Stefani et al. 2011).

5.2 Mitochondrial Shape and Function in Energy Production

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A reduction in the electrochemical potential of mitochondria in cells has emerged as a measure of the mitochondrial functional status, which transduces signals to activate pathways to repair or eliminate defective mitochondria. Proteomics, genomics and bioinformatics revealed that mitochondria consist of over 1000 proteins; the composition is plastic and varies within and across species in response to cellular and tissuespecific organismal demands (Pagliarini et al. 2008; Sickmann et al. 2003; Forner et al. 2006). The origin of the mitochondrial proteome is a combination of bacterial (old) and eukaryotic (new) proteins (Gabaldón and Huynen 2004). The replication and translation of machineries for mitochondrial DNA (mtDNA) differ in their evolutionary origins in bacteriophages (Lecrenier et al. 1997; Stumpf et al. 2011; Tiranti et al. 1997), while the mitochondrial translational mechanism exhibits a distinctive evolutionary relationship to bacteria (Christian and Spremulli 2012). In supplement to protein components, the mitochondrial genome codifies 22 transfer RNAs and two mitochondrial ribosome-coding RNAs, essential components of the genome’s own translation apparatus. The mitochondrial ribosome arrangement in the mitochondrial matrix is a fairly complex and highly regulated process comprising the processing and maturation of mitochondrial ribosomal encoding RNA and the assembly of mitochondrial ribosomal proteins into small and large subunits (Fung et al. 2013). Nevertheless, there is only a fraction of mitochondrial ribosome proteins that possess identifiable homologs in bacteria (Sharma et al. 2003). The roles of mitochondrial specific ribosomal proteins are not comprehended, but it is assumed that these proteins were designed to coordinate mitochondrial translation with extramitochondrial pathways occurring in eukaryotic cells. As with many mitochondrial machines, the ribosome represents a mixture of old and new innovations (Friedman and Nunnari 2014). The nuclear-coded proteins constituting the major of the mitochondrial proteome are translated on cytosolic ribosomes and actively imported by outer and inner membrane translocase machines and sorted into mitochondrial subdomains depending on the electrochemical potential (Neupert and Herrmann 2007; Schmidt et al. 2010). Transcriptional, posttranscriptional and post-translational regulatory mechanisms exist for nuclear-coded mitochondrial proteins. In humans, transcriptional regulation of mitochondrial biogenesis is achieved through the action of the PGC-1 family of coactivators, which react to alterations in nutrient status, such as NAD+/NADH and AMP/ATP ratios (both recorded by SIRT1 and AMPK) and environmental factors (Jäger et al. 2007; Jeninga et al. 2010). Interactions between PGC-1 co-activators and specific transcription factors, such asNRF1, NRF2 and ERR, balance and determine the key functional pathways in mitochondria. These interactions coordinate the nuclear and mitochondrial genomes through the induction of nuclear genes that directly affect the preservation of mtDNA (Scarpulla et al. 2012). The evidence in yeast indicated that nuclear transcribed messenger RNAs coding for mitochondrial proteins are post-transcribed in a highly regulated spatial and temporal manner at the mitochondrial outer membrane and translated coordinately (Gadir et al. 2011; Garcia et al. 2007). The knowledge of pathways underlying molecular mechanisms of mRNA targeting to mitochondria is suggested to be crucial in polarized cells such as neurons. Cytosolic kinases are capable for post-translational modifications, such as phosphorylation, of import machinery for mitochondrial components and hence can even fine-tune the proteome due to metabolic requirements (Schmidt et al. 2011). Mutations in mtDNA genes or nuclear genes encoding the mitochondrial proteins essential for aerobic production of ATP evoke a multifaceted and often disastrous array of human mitochondrial diseases that can target any organ in the body at any time in a person’s life (Nunnari and Suomalainen 2012). Mitochondrial diseases are characterized by a large degree of clinical heterogeneity. Some of these heterogeneities can be attributed to the fact that human cells may have a variable ratio of mutant and wild mtDNA, a condition known as heteroplasia. This applies to mtDNA mutations in proteincoding regions of the mitochondrial genome, where an increase in mutant workload leads to more severe disease phenotypes. Disease heterogeneity, which cannot be attributed to heteroplasia, also

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appears when mutations are detected in non-coding mitochondrial tRNAs. In particular, mutations in genes with a common function, such as genes encoding subunits of complex I of the respiratory chain, manifest very different diseases, such as optic nerve atrophy in adults or encephalopathy in infants (Wallace et al. 1988; Morris et al. 1996). There appears to be a correlation between mutations in mtDNA and aging, possibly due to mtDNA-bound defects in somatic stem cells (Ross et al. 2013; Ahlqvist et al. 2012). The role of mitochondria in diseases has been broadened beyond the respiratory chain, since mitochondrial aberrations in their functions and behaviors have been associated with cancer, metabolic disorders and neurodegenerative diseases, including Alzheimer’s, Parkinson’s and Huntington’s disease (Nunnari and Suomalainen 2012). In any case, generally speaking, our current knowledge on the underlying connection between the mitochondrial phenotype and disease is limited and warrants a better comprehension of mitochondrial organization and of the compounds that mitochondria exhibit with the nuclear genome and extra-mitochondrial pathways in different cell types and at the organismic level. To remedy this deficit, a revival in mitochondrial research has begun, accelerated by recent advances in genetics, systemic approaches and our capacity to image mitochondria in high temporal and spatial resolution.

5.3

Mitochondrial Chromosome

In view of the relevance of mtDNA-encoded genes for mitochondrial function, it is not astonishing that there are special mechanisms controlling the structure and distribution of mitochondria and mtDNA actively; whereas in higher eukaryotes, these mechanisms differ from those of their ancestors (Nunnari and Suomalainen 2012). Dissimilar to bacteria, in the majority of the cell types, individual mitochondria are not detectable, in contrast, they embrace an interconnected network that comprises multiple copies of the mitochondrial chromosome and forms a syncytium (Fig. 5.2). Similar to bacterial and nuclear chromosomes, mtDNA is highly concentrated within the mitochondrial matrix, and therefore mtDNA–protein complexes can be seen as punctate structures in these networks, which are known as nucleoids. The mechanism of mtDNA condensation has been elucidated by researchers who have revealed the crystal structure of the most common mtDNA-associated protein in mammalian cells, the mitochondrial transcription factor A (TFAM). The structure demonstrates that TFAM both binds and bends short sections of mtDNA and forms loops facilitating mtDNA packaging (Ngo et al. 2011; Rubio-Cosials et al. 2011). TFAM also fulfills a crucial role in mtDNA transcription, and its expression guidance of mtDNA copy levels in cells renders it a key regulator in the maintenance and transmission of mtDNA (Ekstrand et al. 2004; Shi et al. 2012). Additional proteins, which are decisive for the maintenance of mtDNA, are pinpointed on nucleoids. Among these proteins are replication and repair machines, which in humans contain DNA polymerase c and its secondary proteins, such as the replication helicase twinkle (Bogenhagen 2012). Mutations in genes coding for these and additional factors essential for mtDNA maintenance result in a wide range of human mitochondrial diseases, even though it is at the molecular level not clear how these and the multiple other identified mtDNA-associated proteins are assembled and organized to form nucleoids (Copeland 2012). For many species, proteomic populations of mtDNA-associated proteins have been identified (Bogenhagen et al. 2003; Kaufman et al. 2000; He et al. 2012). Both the higher-order nucleoid organization and the mode of mtDNA replication are varying between biological kingdoms, leading to greater complexity. All these differences are a direct result of the components of the genome and nucleoid. The yeast, for instance, owns an active recombination machine in the same way as Rad52 recombination proteins, and replication is expected to be primarily achieved by recombination (Mbantenkhu et al. 2011). Compared to the human mitochondrial

5.3 Mitochondrial Chromosome

145

Fig. 5.2 Fission and fusion of mitochondria due to environmental cues, such as severe or mild external stresses

genome, the larger 80 kilobase yeast genome is therefore packaged in several copies within the nucleoid. Conversely, the mechanism of human mtDNA replication is in most tissues independent of recombination and is achieved by strand shifting (Holt and Reyes 2012, Brown et al. 2008). Highresolution imaging repeatedly demonstrates that human nucleoids retain a comparatively small number of mtDNA molecules and are therefore more solitary (Brown et al. 2011; Kukat et al. 2011). These differences in the organization and transmission modes of the mitochondrial chromosome have a major impact on the segregation behavior of mtDNA.

5.4

Segregation Modes of the mtDNA

Multiple copying of mtDNA denotes that the mode of mtDNA transmission is considered stochastic or relaxed in most cell types and differs radically from that of nulcear genes (Birky 2001). At organismic level, including humans, the mtDNA is inherited in a uniparentally on the mother’s side and paternal mtDNA undergoes active destruction after fertilization (Al Rawi et al. 2011; Sato and Sato 2011). Mitochondria in oocytes and sperm vary in functional states, morphology and cellular distributions, and these variations are presumably important to increase fitness. In other respects, the mtDNA genotypes separate rapidly from generation to generation to eliminate severely damaged mitochondria and/or mtDNA. In line with this, oocytes have a bottleneck partly due to the way mtDNA is replicated and as a result of mitochondrial organization. The oocyte mitochondria are arranged in a temporary structure, which is known as Balbiani body, consisting of other organelles, such as the endoplasmic reticulum (ER) and Golgi apparatus, however the biogenesis of this structure is barely known (Pepling et al. 2007). When reprogramming fibroblasts to induced pluripotent stem cells, heteroplasmic mtDNA genotypes also separate through a bottleneck and mitochondria are clustered into a Balbiani-like structure (Hämäläinen et al. 2013), indicating that induced pluripotent stem cells have the potential to be a useful instrument to explore the mechanisms of mtDNA genotype segregation during development on cellular and molecular length scales.

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Nuclear genes are replicated during a S-phase of the cell cycle and segregated by the combined action of a microtubule-based spindle apparatus and an actin-driven cytokinesis machinery, which lead to the physical partition of the chromosomes into the two daughter cells. Bacterial cells also exhibit cell cycle mechanisms to synchronize cell division with chromosome segregation by a tubulin-like FtsZ cell division mechanism. In contrast, the replication and segregation of mitochondrial chromosomes in most eukaryotes are not necessarily linked to the cell cycle and hence mtDNA replication can be performed for solely a subset of nucleoids at various time points (Meeusen and Nunnari 2003). Since the bacterial cytoskeletal machinery has been lost during the evolution of mitochondria, a question can be raised: What mechanisms are employed to locate sites of division and segregate mtDNA? These mechanisms are likely to be relevant to understanding the cell- and tissuespecific mechanisms underlying diseases associated with specific mtDNA mutations.

5.5

Dynamin-Facilitated Mitochondrial Dynamics

In higher eukaryotes, the segregation of mtDNA on the cellular level relies in part on continuous division and fusion events (Figure 5.3), whose rates tend to respond to the demands of a distinct cell type (Hoppins et al. 2007). A fundamental role of mitochondrial fusion is in facilitating communication between organelles, possibly to provide access to mtDNA expression products (Chen et al. 2007, 2010; Hermann 1998). Mitochondrial fusion is a mechanism to cushion transient defects occurring in mitochondria (Rolland et al. 2013). The division of mitochondria counteracts the fusiondriven network mitochondrial assembly to promote their distribution and transport by motor proteins on cytoskeletal pathways to and from various distinct locations (Verstreken et al. 2005). An equilibrium between division and fusion is crucial for the distribution and conservation of mtDNA. The loss of mitochondrial fusion leads to a fragmentation of a normally connected network into numerous small mitochondria due to the non-consistent division and the loss of mtDNA either in whole or in part in cells with the consequent severe defects in oxidative phosphorylation (Hermann 1998; Chen et al. 2005). The reduction of mitochondrial division induces the elongation of mitochondria and the assembly of highly cross-linked reticular structures, along with defects in oxidative phosphorylation and the loss of mtDNA during cell division (Ishihara et al. 2009; Parone et al. 2008; Wakabayashi et al. 2009; Hanekamp et al. 2002). The connection between mitochondrial dynamics and mtDNA transmission is in line with the elementary role of dynamics in mitochondrial copy control. The more distributive character of mitochondrial division combined with opposite fusion has therefore advanced to substitute the old bacterial cytoskeletal machines.

Fig. 5.3 Fission and fusion of mitochondria are highly conserved and contribute to the mitochondrial organization of higher eukaryotes

5.5 Dynamin-Facilitated Mitochondrial Dynamics

147

The mitochondrial division and fusion events are driven by the highly conserved dynamin-related proteins (DRPs), which, through their ability to self-assemble and hydrolyze GTP themselves, assist the remodeling of various intracellular membranes (Faelber et al. 2013). The division of mitochondria is catalyzed by a single DRP, the DRP1 in mammals and Dnm1 in yeast. DRP1 and Dnm1 build up helical structures through stalk domains that are twines around the outer surface of mitochondria at the constriction sites, whose diameters are identical to the diameters of the division helix (Ingerman et al. 2005; Labrousse et al. 1999). Moreover, interactions between the GTPase domains of division DRPs spiral-shaped sprouts promote the hydrolysis of GTP, which seem to be designated for conformational alterations transmitted by the DRP helix to enable coordinate division of the outer and inner membranes (Ford et al. 2011; Fröhlich et al. 2013; Faelber et al. 2011). The fusion of the outer and inner membranes of the mitochondria necessitates two evolutionarily distinct integral membrane proteins, DRPs, which are synonymously termed Mitofusin (MFN)1/MFN2 in mammals or Fzo1 in yeast, and OPA1 in mammals (Mgm1 in yeast) necessary (Meeusen et al. 2004). It is still largely unclear how the fusion of the DRPs is carried out at a mechanistic level, despite the fact that interactions between the GTPase domains on opposite membranes seem to be used for membrane anchoring and that selfassembly through the proposed stalk-like regions within a membrane can be employed for fusion. The DRP family has its origin in bacteria, implying that the members also operate in membranelinked processes (Low et al. 2009; Lewis et al. 2016). However, the acquired roles of DRPs as primary engines controlling the mitochondrial copy number represent a radical aberration from the bacterial FtsZ-dependent division machine that operates from the cytosolic face of the plasma membrane to facilitate constriction and cleavage. The cleavage machines of primitive eukaryotic organelles in organisms, such as the red alga cyanidioschyzon merolae, and of endosymbiotic plastids and chloroplasts in most photosynthetic eukaryotic organisms yield insights into this transition to DRP-controlled cleavage. These endosymbiotic organelles contain hybrid division machines with both internal FtsZ and external DRP components (Osteryoung and Nunnari 2003; Nishida et al. 2003). The FtsZ engine operates by narrowing and splitting the inner and outer membranes and the division site; whereas, the DRP engine is engaged on the outer surface of the organelle, in particular, at the narrowing, and operates relatively late in the process to complete the cleavage of the outer membrane (Ji et al. 2017). In bacteria, the FtsZ-driven placement of the division site in these mitochondria and plastids is critical for the transfer of organellar genomes.

5.6

Endoplasmatic Reticulum-Driven Division of the Mitochondrion

The loss of the FtsZ-like mechanism in higher eukaryotes lets to the questions of how and where division sites are positioned in mitochondria and whether the placement of the division site is critical for mtDNA transfer. These questions can be partially answered, since a key interorganellar interaction with the ER is responsible to the positioning of the mitochondrial division site (Friedman et al. 2011). Before DRP1 recruited to the mitochondrial outer membrane, the ER tubules are entwined around mitochondria and thereby determine the sites of mitochondrial division, which is a highly conserved (from yeast to humans) phenomenon that is known as ER-associated mitochondrial division (ERMD). At these points, the mitochondria are narrowed and hence geometric prerequisites are created for the assembly of the division DRP helix. In the next step, the integral outer membrane DRP1 receptor and effector MFF (Otera et al. 2010) are recruited to the contact sites, which delivers a spatial linkage of DRP1 recruitment with its activation and assembly into a division process. However, the underlying mechanism of this ER–mitochondrial microdomain and the ER-driven mitochondrial constriction is not still unclear. The ER may be able to directly change the composition and/or morphology of the

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mitochondrial membrane to regulate the recruitment of factors localized on the outside and/or inside of mitochondria performing the narrowing and the division. In mammals, the actin cytoskeleton has been involved in ERMD, possibly through the ERlocalized isoform of the formin INF2, which leads to the question of whether the mitochondrial constriction during the division is regulated by actin (Korobova et al. 2013). Additionally, the ERMD must include a linkage or tether between the mitochondrion and the ER. In yeast, the molecular basis of this connection has been revealed and is facilitated by a multi-protein complex, which is known as ER–mitochondrial encounter structure (ERMES). The ERMES provides a discrete and finite number of connections between the ER and mitochondria (Kornmann et al. 2009, Murley et al. 2013). Besides marking the division sites, ERMES is tightly coupled to a subset of nucleoids involved in the replication of mtDNA (Meeusen and Nunnari 2003, Hobbs et al. 2001) and acts as components of a larger structural network connecting multiple membranes. At sites of an ERMES complex, the nucleoids separated by an unknown mechanism and mostly are localized into both ends of divided mitochondria (Murley et al. 2013). The ERMES complex is included in a bridge between mitochondria and the actin cytoskeleton, implying that it can connect and coordinate the nucleoid segregation, mitochondrial narrowing during division and mitochondrial distribution after division (Boldogh et al. 2003). Hence, the process of ERMD and nucleoid segregation in yeast seems to be crucially related to the role of actin in ERMD in mammals. After ERMD in yeast, the distribution of daughter mitochondria demands the highly conserved Miro GTPase Gem1 (Murley et al. 2013). Gem1 appears to promote with ERMES the cleavage of daughter mitochondria by attracting motility factors to mitochondrial tips after division. Metazoan Gem1 orthologues, MIRO1 and MIRO2 (synonymously referred to as Miro), similarly act in the distribution of mitochondria. Thereby, Miro proteins seem to connect mitochondria to a Milton/TRAK protein family of kinesin-1 adaptors member for their microtubule-based transport (Glater et al. 2006; Fransson et al. 2006). Although the Miro GTPase family is highly conserved, the mitochondrial transport mechanisms are distinct in mammals from yeast, as the mammalian mitochondrial motility is actin-based. Hence, the Gem1 and Miro GTPases may provide the motility in the distribution of mitochondria by directly facilitate molecular tethers to disengage mitochondria from the ER at division sites. However, Gem1-driven distribution of daughter mitochondria after division represents an intracellular mechanism for the coordinated mitochondria and mtDNA distribution.

5.7

What Determines the Division of the Mitochondria?

The position of ERMD is in the division plane neighboring mitochondrial nucleoids to influence their distribution into newly produced daughter mitochondria. In mammalian cells, nucleoids are positioned similarly to ERMD at mitochondrial division sites and tips (Iborra et al. 2004; Garrido et al. 2003), and in the absence of the division, DRP1 nucleoids cluster within hyperfused mitochondria (Ban-Ishihara et al. 2013). This finding leads to the hypothesis that ERMD’s role in the nucleoid distribution is highly conserved, although the molecular nature of the tethers between the ER and the mitochondria at division sites in mammals is not yet clear. What regulates the localization of division sites? It is still not clear what prerequisites are necessary for the number and localization of ER– mitochondria interaction sites, which are associated with the nucleoid segregation. These questions refer to whether there exists a mechanism in mitochondria capable of easing mitochondrial division, in an analogous manner to bacterial FtsZ. In yeast, the inner membrane protein Mdm represents a good candidate for the internal membrane cleavage mechanism (Bogenhagen et al. 2003), which comprises matrix-localized coiled-coil regions operating in trans across inner membranes to confer constriction (Messerschmitt et al. 2003).

5.7 What Determines the Division of the Mitochondria?

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Based on the endosymbiotic origin of mitochondria, it seems to be likely that a spatial mark inside the organelles exists for the positioning of division plane. There is indeed evidence for an autonomous structure within the mitochondria that is similar to the DNA-replicating replisome acting as such a marker. In the absence of mtDNA in yeast and mammalian cells, nucleoid proteins necessary for mtDNA management stay localized on discrete spotted structures in mitochondrial tubules, implying an intrinsic potential to self-organize in a mtDNA-independent structure (Meeusen and Nunnari 2003; Spelbrink et al. 2001). In yeast, the replisomes dissociate within mitochondria and persist their link with ERMES-marked ER–mitochondria interaction sites even if there are no mitochondrial genomes (Meeusen and Nunnari 2003). Mitochondrial skeletal structures can additionally act as internal spatial marks. Although bacteriallike cytoskeletal elements seem to have been lost, there exist still several scaffold-like structures in mitochondria, which aid the formation of their intricate external and internal structure. Mitochondrial scaffolds collaborate to establish a higher-level organization of the organelle encoding spatial labels for the placement of nucleoids and/or division sites. These scaffolds incorporate the conserved prohibitin complex, forming ring-like structures in the inner membrane, which, together with mitochondrial lipids, such as cardiolipin and phosphatidylethanolamine, enable the organization of the domains of the inner membrane (Osman et al. 2011). Another primary skeletal element in mitochondria represents the conserved multi-component inner membrane-associated complex MitOS (synonymously termed MICOS and MINOS) (Hoppins et al. 2011a; Harner et al. 2011; von der Malsburg et al. 2011). Evidence suggests that MitOS constitutes an extended heteromorphic structure organizing and shaping the mitochondrial inner membrane, which is subdivided into several regions exhibiting structural, compositional and functional diversities. The region in close proximity to the outer membrane, known as the boundary region, promotes the traffic of lipids, the import of mitochondrial proteins and the assembly of the respiratory complex. Inner membrane cisternal invaginations, known as cristae house, build respiratory complexes and created strongly curved edges, which are fixed by dimerization or multimerization of ATP synthase complexes (Davies et al. 2012). Rather narrow tubules, named crista junctions, associate cristae with the boundary membrane and remove soluble intermembrane space components from the boundary regions. In apoptosis, these junctions undergo restructuring to evoke the release of intermembrane space-localized cell-death inducers into the cytosol during mitochondrial outer membrane permeabilization (MOMP) (Frezza et al. 2006). Super-resolution imaging of mammalian nucleoids highlighted that they are closely coupled with inner membrane cristae (Brown et al. 2011). Thus, the MitOS complex can also fulfill a prominent role in nucleoid positioning and can partly contribute to a spatial label connecting the inside of mitochondria to the outside. In accordance with this possibility, elements of the MitOS complex appear in the yeast adjacent to nucleoids, and the loss of an intact MitOS complex results in the aggregation of the nucleoids (Itoh et al. 2013). This complex also conveys mitochondrial biogenesis through association with components of the import and sorting machineries within the outer mitochondrial membrane (van der Malsburg et al. 2011). Thus, MitOS seem to possess a broader global function in the form of a blueprint of the mitochondrial organization and may serve as a formto-function integrator in this sense.

5.8

Role of the ERMD Microdomain

Although the basic role of ER-associated division seems to be the facilitation of the mtDNA distribution, there is evidence that ERMD domains are utilized for additional and diverse functions in cells and can therefore act as integrators. In addition to the ERMD, the ERMES complex is functionally involved in the biogenesis of outer membrane proteins and in lipid transport between the ER

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and mitochondria, which is essential for the synthesis of important mitochondrial lipids, such as phosphatidylethanolamine and cardiolipin (Kornmann et al. 2009; Voss et al. 2012). ERMD domains could therefore also control the cellular status by providing the communication between mitochondrial behaviors and cellular signaling pathways, such as mitochondrial division, fusion and cell death. In line with this is the fact that DRP1 drives the recruitment and activation of the pro-apoptotic Bcl-2 protein BAX to the mitochondrial outer membrane to confer MOMP (Montessuit et al. 2010). In addition, ER-synthesized sphingolipids support in vitro the mitochondrial assembly of BAX and the activation of MOMP (Chipuk et al. 2012). In contrast, the mitochondrial fusion holds a negative regulatory role in MOMP since cytosolic BAX stimulates mitochondrial fusion in vitro via the DRP MFN2 (Hoppins et al. 2011b), which increases the chance that fusion DRPs also govern apoptosis by ERMD domains. The ERMD domains seem to span both into inner mitochondrial compartments and the ER lumen to accommodate the functional status of both organelles, as proposed for the regulation of MOMP under ER stress. In accordance with this, the apoptotic regulator CDIP1 works together with the ER protein BAP31, when the ER is stressed resulting in a BAX arrangement on mitochondria (Namba et al. 2013). Therefore, it needs to be revealed whether the interaction sites of BAP31 match with sites of interaction with ERMD. The relationship between the ER and mitochondria is evidenced by their role in multiple diseases linked to altered mitochondrial dynamics. Among them are Huntington’s disease, optic atrophy and spinocerebellar ataxias (Schon and Przedborski 2011, in which proteins are targeted to ER–mitochondrial interaction sites. In line with this, alteration of ER–mitochondrial interaction has been observed in Alzheimer’s disease (Area-Gomez et al. 2012; Hedskog et al. 2013). Finally, the dysfunction of the ERMD domain seems to be a major contributory factor in many diseases. However, the ERMD domains are solely one special type of ER–mitochondrial interaction. In yeast, for example, the ER is part of two distinct Num1 and Mmr1 bonds that selectively direct the mitochondria to the cortex of mother and daughter cells independently of ERMES and ERMD (Lackner et al. 2013; Swayne et al. 2011). In addition, the fusion DRP MFN2, which is not required to the formation of the ERMD contact, seems to function in mammalian cells as an ER–mitochondrial bound (Friedman et al. 2011; de Brito et al. 2008). However, there is still more effort necessary, especially in mammalian cells, to uncover the molecular basis of ER–mitochondria interactions and whether specific interaction sites and bounds are formed for distinct shared ER–mitochondrial functions, including lipid biosynthesis, Ca2+ homeostasis, ERMD and autophagy (Rowland et al. 2012; Hamasaki et al. 2013). This is an exciting research field in mitochondrial biology that is regarded of great importance for the understanding of the etiology underlying mitochondrial dysfunction-related diseases.

5.9

Coordination of Various Mitochondrial Behaviors

Mitochondrial division and fusion represent major regulatory elements of the mitochondrial distribution; however, the mitochondrial network in cells is driven by additional activities, such as tether formation and motility. Neuronal cells represent an example of how the mitochondrial behavioral networks function coordinately to maintain cellular functions. More precisely, neurons exhibit a long cell shape and its interior is highly compartmentalized and for their appropriate function mitochondria are required to be distributed to fulfill the specific spatial and temporal demands of cells. At the ends of axons, increased mitochondrial ATP production and Ca2+ buffering are needed. The ends of the axons are subject to dynamic remodeling of their structures, which necessitate the pinpointed localization of mitochondria for the synaptic transmission. Since the mitochondrial biogenesis is

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found mainly in the neuronal stoma, active mechanisms are needed either for the transport or the immobilization of mitochondria at the distal synaptic ends. Results on neuron-specific protein syntaphilin, which binds specifically to the mitochondrial outer membrane and interacts with immobilized axonal mitochondria at active terminal ends of synapses, have yielded new insights into how these two diverging processes are coordinated and function together to selectively align mitochondria at active synaptic ends (Chen et al. 2013). Mitochondria targeted for axons are produced by mitochondrial division in the soma and then transported along the microtubules to the synapse. The spatial couplings between division and nucleoids, and nucleoids and cytoskeletal elements, seem to help to direct mtDNA to mitochondria labeled for transport in neurons. Syntaphilin acts as a brake, and therefore it employs at least two distinct mechanisms. Firstly, it combines directly to the microtubule-based kinesin motor KIF5 in vitro and thereby impairs its motor activity by potentially transforming KIF5 into a component of a static microtubule-dependent mitochondrial attachment, such as a tether. However, whether the ER fulfills a role in the syntaphilin– KIF5 tether biogenesis remains an important question. To ease tethering, syntaphilin also rivaled for KIF5 binding with the adaptor Milton/TRAK. Therefore, a comprehensive interaction between the motility and tethering mechanisms exists to regulate mitochondrial distribution in an activity dependent and spatially specific way. The mitochondrial dynamics are presumably also coordinated at a molecular level with the transport and tethering systems in various cell types. In mammalian cells, the mitochondria are devoid of the fusion DRP MFN2 and hence exhibit severe defects in motility (Baloh et al. 2007), comparable to the motility defects found in cells lacking Miro. This result is not surprising, since MFN2 should physically engage with Miro and Milton/TRAK (Misko et al. 2010). This connection between mitochondrial fusion and motility appears to be necessary for understanding why mutations in MFN2 and OPA1 in humans evoke the tissue-specific neurodegenerative diseases, Charcot Marie Tooth Type 2A (CMT2A) and dominant optic atrophy (DOA), respectively (Nunnari and Suomalainen 2012). With a high frequency, mutation in mtDNA and nuclear genes leads to mitochondrial dysfunction, which can selectively occur in neurons and contribute to various neurodegenerative diseases (Copeland 2012).

5.10

How are Mitochondrial Pathways Controlled?

Mitochondrial behaviors are part of multiple stress or quality control pathways in cells that perceive and respond to mitochondrial and cellular dysfunction. More precisely, within mitochondria, molecular chaperones and quality control proteases work together to facilitate the assembly of protein complexes consisting of mtDNA- and nucleus-encoded proteins, to monitor the unfloding of proteins and subsequently to degrade them (Baker et al. 2011). The imbalance between nuclear and mitochondrial proteomes and/or the accumulation of unfolded mitochondrial proteins induces a transcriptional response in metazoans, which is known as the unfolded protein stress response pathway in mitochondria (UPRmt) (Houtkooper et al. 2013; Zhao et al. 2002; Martinus et al. 1996; Melber et al. 2018). Moreover, UPRmt appears to be associated with several age-based diseases, including Alzheimer’s disease, Huntington’s disease and Parkinson’s disease (Kikis et al. 2010; Jovaisaite et al. 2014). To restore the homeostasis of the organelle, a response is triggered by signals generated at the mitochondrial level regulating the transcriptional activation of nucleus-encoded mitochondrial chaperone genes and others (Melber et al. 2018). The pathway has been molecularly identified in Caenorhabditis elegans requiring both the mitochondrial inner membrane peptide transporter HAF1 and the bZip transcription factor ATFS1 for the UPRmt signal transduction process (Haynes et al. 2010). In the mitochondria of healthy cells, ATFS1 is actively imported into the mitochondrial

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matrix, where it is constitutively degraded (Nargund et al. 2012). When the electron transport chain is interrupted, the membrane potential is diminished and thus the import efficiency of ATFS1 is reduced in a way that depends to some extent on HAF1. ATFS1 localized outside the mitochondria is stabilized and enters the nucleus, in which it induces the transcription of distinct genes, such as mitochondrial chaperones and the import machinery, and leads to a remodeling of the metabolism to be even less dependent on the respiratory system. In C. elegans, the UPRmt activation correlates with an extended lifespan, and there is some support for the hypothesis that activation of this pathway in mammals also prolongs their lives, indicating that mitochondria are a key element in aging, which further identifies mitochondria as a crucial factor in aging (Houtkooper et al. 2013). However, it needs to be clarified whether the molecular mechanisms that drive the UPRmt are conserved in mammalian systems. Perturbations in the function of the electron transport chain and/or a decrease in the membrane potential can be induced by additional mitochondrial stress-induced pathways. In detail, the mitochondrial inner membrane fusion DRP OPA1 functions as a switchover between two pathways. In normal and healthy cells, OPA1 processing is constitutively performed by the i-AAA protease YME1L to produce both long transmembrane connected and short soluble isoforms necessary for membrane fusion (Griparic et al. 2007). The progressive decrease of mitochondrial membrane potential allows on the one hand the metalloprotease OMA1 to transform the long OPA1 isoforms into short isoforms, impairing mitochondrial fusion and subsequent mitochondrial fragmentation (Head et al. 2009; Ehses et al. 2009). On the other hand, another stress-induced response, known as mitochondrial hyperfusion, requires long OPA1 isoforms. More specifically, mitochondrial hyperfusion facilitates the assembly of a highly cross-linked mitochondrial network and designed to mitigate the potentially harmful effects of stress, including UV irradiation and nutrient deficiency. During starvation, hyperfusion seems to prevent mitochondria from autophagic degradation or mitophagy by steric obstruction (Rambold et al. 2011; Gomes et al. 2011). Additionally, mitochondrial hyperfusion also provides a homeostatic function to preserve ATP production, when the complex IV of the electron transport chain is disrupted (Rolland et al. 2013). Nevertheless, the hyperfusion response is transient and therefore cannot compensate long-term defects in the activity of the electron transport chain. A more terminal reaction to mitochondrial dysfunction is mitophagy, a condition that is also caused by a decrease in membrane potential-based protein import. In this process, the kinase PINK1 is transported into healthy mitochondria and undergoes progressive degradation. A mitochondrial dysfunction-based reduction in import leads to PINK1 accumulation on the outer membrane, where it attracts the E3 ligase Parkin (Matsuda et al. 2010; Narendra et al. 2010). Parkin’s target molecules for ubiquitination compromise the transport factor Miro and the mitochondrial fusion protein MFN2 (Wang et al. 2011; Chan et al. 2011; Tanaka et al. 2010). The proteasomal degradation of ubiquitinated mitochondrial outer membrane proteins is based on the AAA-ATPase p97 in a similar way to ER-associated degradation (Tanaka et al. 2010; Xu et al. 2011). Parkin-facilitated degradation of factors associated with mitochondrial motility and fusion increases the selective removal of defective mitochondria by autophagy. In supplement to detailing defective mitochondria for degradation, an in vivo study in Drosophila points out that the PINK1– Parkin pathway may also be selectively targeting respiratory complexes for break-down (Vincow et al. 2013). In furtherance of this assumption, the selective targeting of complex I for dissociation has also been detected in cell culture models (Hämäläinen et al. 2013), whereas the mechanisms driving this phenomenon are still elusive. Mitochondrial stress pathways seem to fulfill crucial roles in the manifestation of diseases involving mitochondrial dysfunction and in turn may represent interesting therapeutic targets. The PINK1–Parkin mitophagy pathway has mutations in each gene that are associated with genetically inherited Parkinson’s disease (Kitada et al. 1998; Valente et al. 2004), meaning that this pathway is of

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How are Mitochondrial Pathways Controlled?

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relevance. However, are all these stress-based pathways including those with a reduction of the membrane potential physiologically relevant? In contrast to these multiple stress pathways, mtDNA mutations can concentrate in differentiated post-mitotic cells, at the cost of functional wild-type mtDNA, subsequently causing diseases (Sterky et al. 2011). Therefore, animal models for diseases based on mitochondrial dysfunction will be developed in the future to demonstrate the physiological contributions of these pathways. Further basic biological research is required to investigate how the cell properly senses, activates and coordinates these pathways differentially, both among themselves and with other signal transduction pathways, including cell death-related processes. There is no clarity as to how cells adequately assimilate the UPRmt and mitophagy pathways, both governed by the basic import level. Beyond that, it is not known whether OPA1 can work as a molecular integrator of stresses or purely as a simple modulator of mitochondrial shape or how OPA1-driven stress pathways are synchronized with UPRmt and mitophagy. The resulting view of mitochondria is that of a highly organized structural area for construction of an organelle, which behaves to respond to cellular needs and to its own dysfunction. The combination of system-based approaches with super-resolution microscopy and new genetic tools will clarify our understanding for the molecular basis of mitochondrial structure. Precisely as mitochondrial superorganization is constructed, the fundamental question will be whether the primary determinants of organization emanate from within the organelle itself and are closely related to the oldest trait, the genome.

5.11

Controversial Discussion of the Drp1/Dnm1-Independent Mitophagy

It is still a major issue and controversially discussed whether or not mitophagy relies on prior mitochondrial fragmentation facilitated by the canonical mitochondrial division process. In detail, it has been reported that mitochondrial fragments can begin to bud and divide from mitochondrial tubules, when they are tightly connected with autophagosomes; whereas, the mitochondrial division factor Drp1/Dnm1 is not required (Yamashita et al. 2016). The process of macroautophagy is usually referred to simply as autophagy, which constitutes a highly conserved catabolic pathway that is crucial to intracellular homeostasis, quality control and responses to stress. A hallmark of autophagy are double-membraned vesicular structures known as autophagosomes, which assemble de novo and convert parts of the cytoplasm into lysosomes or vacuoles for degradation and recycling (Feng et al. 2014). A multi-component core autophagy system appears to coordinate membranes and membrane-remodeling components of diverse origin to propagate nucleation, extension and sealing of a single-membrane structure, as well as the isolation of a membrane, to build the double-membrane layered autophagosome. The process of autophagy can take place without any selection pressure, with autophagosomes randomly enclosing parts of the cytoplasm. However, there are selective autophagy modes that permit the cells to target distinct cargoes in a preferred manner or even explicitly for degradation. Selectivity is obtained by receptorbased physical interactions that couple the autophagy system with the cargo, resulting in the formation of an isolation membrane that closely wraps the bound cargo. The process of selective turnover of mitochondria by autophagy, which is synonymously termed mitophagy, is crucial in the quality control of mitochondria (Graef 2016). Mitochondria commonly assemble to highly dynamic networks of interconnected tubules that constantly fuse and divide. The structural and functional integrity is obtained by fusion and division of the outer and inner membrane of mitochondria and is performed spatially and temporally coordinated that is facilitated by evolutionarily conserved dynamin-related GTPases such as Mfn1/2 and Fzo1 and Opa1 and Mgm1 for fusion and Drp1 and Dnm1 for division in mammals and yeasts,

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respectively. During the division of mitochondria, Drp1/Dnm1 is recruited into mitochondria by the effectors/adaptors, such as Mdv1 and Fis1 in yeasts and Fis1, Mff, MiD49 and MiD51 in mammals. In its GTP-bound state, Drp1 assembles into spiral-shaped oligomeric structures entwined around mitochondrial constrictions that are created and labeled by the ER (Lackner 2014). The hydrolysis of GTP causes conformational alterations in Drp1/Dnm1 compounds that finally lead to the helical constriction and division of the two mitochondrial membranes. The mitochondrial architecture is closely determined by the functional state of the organelle and the entire cell. Hence, the mitochondrial fragmentation based on increased Drp1/Dnm1-driven division is generally accompanied by mitochondrial stress and dysfunction (Nunnari and Suomalainen 2012). In mammals, dysfunctional mitochondria cause activation of the ubiquitin ligase parkin and the ubiquitin kinase PINK1 (the PINK1–parkin pathway), which thereby produce phospho-ubiquitinated mitochondrial outer membrane proteins. Phospho-ubiquitin acts as a signal for autophagy and attracts the core autophagy system via the two mitophagy receptors, NDP52 and optineurin (Narendra et al. 2008; Lazarou et al. 2015). In other respects, the mitochondrial outer membrane proteins Bcl2-L-13 in mammals, and the homolog Atg32 in yeast, also resemble the autophagy system in mitochondrial dysfunction, which leads to the hypothesis whether redundant pathways are available to induce mitophagy (Kanki et al. 2009; Okamoto et al. 2009; Murakawa et al. 2015). Since mitochondria are intrinsically devoured by autophagosomes of an apparently limited size, a pivotal role was assumed for Drp1/Dnm1-driven division in achieving mitochondrial fragments of variable and suitable size for turnover in yeasts and mammals (Kanki et al. 2009; Tanaka et al. 2010; Rambold et al. 2011; Abeliovich et al. 2013; Mao et al. 2013; Kageyama et al. 2014; Ikeda et al. 2015). However, a small number of studies revealed that Drp1/Dnm1-independent mitophagy may be seen in yeasts and mammals (Mendl et al. 2011; Bernhardt et al. 2015; Murakawa et al. 2015). A controversial issue remained whether Drp1/Dnm1-driven mitochondrial cleavage is necessary for mitophagy or not. Thus, the role of Drp1/Dnm1-driven mitochondrial cleavage for mitophagy has been examined in two yeast systems, Saccharomyces cerevisiae and Pichia pastoris, and three different cell culture systems, including HeLa cells, SH-SY5Y cells and mouse embryonic fibroblasts, under various mitophagy-inducing conditions (Yamashita et al. 2016). In both yeasts, a slightly delayed but still pronounced turnover of mitochondria anchoring the Dnm1 protein was observed during starvation in the absence of Dnm1 or Fis1 (Yamashita et al. 2016). This finding suggests a minor role for Dnm1facilitated division during mitophagy, as yeast cells dnm1D and fis1D in fluorescence or electron microscopy exhibited several small mitochondrial fragments in autophagosomes, implying that these small mitochondrial fragments are produced and picked up by autophagosomes apart from the canonical mitochondrial division mechanism. In the next step, various cell culture systems are analyzed for the effect of loss of Drp1-driven mitochondrial division on the process of mitophagy. Mitophagy was therefore principally triggered by exposure of cells to hypoxic environments and by the treatment of cells with deferiprone, an iron-chelating reagent, or by loss of the mitochondrial membrane potential by treatment of cells with carbonyl cyanide m-chlorophenylhydrazone (Yamashita et al. 2016). Under both experimental conditions, turnover of small mitochondrial fragments was demonstrated by mitophagy in the absence of the Drp1-driven mitochondrial division mechanism. All in all, these data suggest that Drp1/Dnm1-independent mitophagy is evolutionarily preserved and appears under a wide array of mitophagia-inducing circumstances. However, these findings lead to two main questions: How can the mitochondrial fragments be present in autophagosomes in the absence of Drp1/Dnm1? Can the Drp1/Dnm1-independent mechanism simultaneously function even in the presence of the canonical mitochondrial division mechanism? Therefore, the assembly of autophagosomes and the presence of mitochondrial fragments in wild-type and Drp1-knockout HeLa cells were monitored during hypoxia-facilitated mitophagy using fluorescence-based live-cell imaging (Yamashita et al. 2016). The biogenesis of

5.11

Controversial Discussion of the Drp1/Dnm1-Independent Mitophagy

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autophagosomes is initiated by mitochondrial tubules and small mitochondrial fragments that bud and divide from mitochondrial tubules simultaneously with the expansion and closure of the isolation membrane to generate autophagosomes regardless of the presence or absence of Drp1 in these cells. In practice, the use of triple color imaging demonstrated that Drp1, as predicted, creates focal points at canonical division sites on mitochondrial tubules, but was not found at mitochondrial bud sites in conjunction with autophagosome formation (Yamashita et al. 2016). Thus, Drp1/Dnm1 is not only unnecessary for mitophagy, but similarly appears to be lacking from sites of mitochondrial narrowing and division during mitophagy. Hence, these findings are crucial for the establishment of a novel view on the process of mitophagy. However, unlike sequential models (Kalia et al. 2018), which suggest that mitochondrial fragments form in a Drp1/Dnm1-dependent mode before they can later be captured by autophagy, the new model is based on the principles that mitochondrial fragments are formed independently of Drp1/Dnm1 and that autophagosome formation and mitochondrial budding and division are spatially and temporally coordinated processes (Yamashita et al. 2016). Since the production of the mitochondrial fragments depends on the autophagy mechanism’s integrity, it can be questioned whether the autophagy mechanism itself facilitates budding and division of mitochondria by nucleation and expansion of the isolation membrane at these sites. However, it is also possible that a mechanism not yet revealed regulates the membrane cleavage in the autophagosome formation during mitophagy. The novel model of the Drp1/Dnm1-independent mitochondrial division during mitophagy raises new questions: How are the sites of autophagosome formation on mitochondrial tubules identified for mitophagy? How mitochondria interact with the autophagy system? After Parkin's accumulation along mitochondrial tubules with increased levels of reactive oxygen species (ROS), the autophagy system is built up in a point pattern corresponding to the focal ubiquitous sites (Yang and Yang 2013). In detail, a short-ranged ROS signal may induce the formation of autophagosomes by precisely targeting ROS-rich regions on mitochondrial tubules by a not yet known mechanism for identifying damaged mitochondria and then facilitating the assembly of the autophagosome. Finally, is it possible to combine both models in a unifying one? Even though the Drp1/Dnm1driven mitochondrial division is not required, it can still promote mitophagy by providing mitochondrial fragments, which are simply targeted for degradation. In addition, the type and strength of the stress on the cells and subsequently on the mitochondria seem to decide whether the Drp1/Dnm1dependent mitochondrial fragmentation is preferred for the turnover of mitochondria through mitophagy or not. When the stress triggers no or only less efficient Drp1/Dnm1-dependent fragmentation, Drp1/Dnm1-independent fragmentation of mitochondria by the autophagy system appears to be the rate-limiting step in mitochondrial turnover by mitophagy. In future experiments, it must to be determined from which types and quantities of cellular stresses one of these two somewhat redundant mitophagy pathways is selected.

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6

Mechanical View on the Mitochondria

Abstract

This chapter presents the cytoplasm and the contained organelles from a biomechanical point of view. Thereby, the entire cell is treated as gel with associated physical features. General physical principles are applied on cells at a mesoscopic length scale. However, on a submesoscopic scale, the mechanical properties of organelles, such as mitochondria, contribute to the overall mechanical properties of the cell. A cell may even respond to the applied mechanical stress by regulating the fusion of fission of the mitochondria. Hence, mitochondria seem to fulfill a key role in mechanotransduction processes in cells. In this chapter, it is firstly described how mechanical factors regulate the structure and function of mitochondria. Secondly, it is presented how the structures of mitochondria impact ATP production and the formation of mitochondrial clusters is described. Thirdly, the properties of such mitochondrial networks are discussed in detail. Fourthly, interaction between the mitochondria and cytoskeletal microenvironment, such as actin filaments, microtubules and intermediate filaments, is highlighted. Fifthly, the mechanobiological aspects of the structure and function of mitochondria are discussed. Finally, it is enlightened how mitochondria are predestinated for providing a mechanical response to external mechanical perturbations.

6.1

Mechanical Factors Regulate the Structure and Function of Mitochondria

The mitochondria serve as a supplier for energy that is provided to the cells as adenosine triphosphate (ATP). Besides this major function, the mitochondria guide the apoptosis, the programmed cell deaths of the cells, support the buffering of calcium and produce the reactive oxygen species (ROS). In order to facilitate all these different functions, mitochondria create an extensive network that contains smaller clusters, which are able to move along microtubules guided by motor proteins, such as dyneins. Mitochondria can interact with the actin cytoskeletal scaffold that is helpful in various cellular responses and for several mechanical factors (Bartolák-Suki et al. 2017). In this chapter, the mitochondrial structure and function are presented and correlated to the cytoskeleton and several mechanical parameters affecting the functions of cells. The morphological characteristics of mitochondria are presented by specifically pointing out to the fission and fusion processes and how the mitochondrial network features guide the function. In particular, the interaction of mitochondria and the cytoskeletal architecture is highlighted, which focuses also in © Springer Nature Switzerland AG 2020 C. T. Mierke, Cellular Mechanics and Biophysics, Biological and Medical Physics, Biomedical Engineering, https://doi.org/10.1007/978-3-030-58532-7_6

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mechanical interactions between these elements. More precisely, it is described how mitochondria stretch and how their dynamic pattern regulates the architectural structure of the mitochondria and subsequently their function. At the end, it is provided how the stiffness of the extracellular matrix environment affects the shape of mitochondria and their generation of ATP. The relatively general role of mitochondria in the process of mechanobiology and how the mechanosensitive properties of mitochondria can induce the onset and further development of specific diseases and cell aging are discussed. The eukaryotic cells contain an extremely complex internal structure to fulfill specialized functions, including the migration of the cells, cell contraction and the division of cells. Moreover, they need to act multiple chemical and mechanical cues evoked by the microenvironment. The cell functions and activities require energy of the mitochondria that is provided in the form of ATP through the oxidative phosphorylation cycle. Besides this key role, mitochondria facilitate the management of apoptosis, buffer calcium and generate ROS (Kluge et al. 2013). To facilitate diverse cell functions, the mitochondria built an expanding interrelated network within the cell that contains somewhat smaller clusters of mitochondria. These smaller clusters possess the capacity to move along the cytoskeletal fibers driven by motor proteins (Boldogh and Pon 2007). In precise detail, the mitochondrial architecture itself is undergoing a highly dynamic transformation as it is continuously subject to division and fusion (Bereiter-Hahn and Voth 1994; Palmer et al. 2011). All these fundamental processes are essential for either the cell integrity or the survival of the entire organism. When proteins, which facilitate the fission (Ishihara et al. 2009) or fusion of mitochondria (Chen et al. 2003) in mice, are genetically knocked out, they produce embryos that are dead before their birth. Although there is strong knowledge about the mitochondrial framework or cluster properties and their dynamic remodeling (Palmer et al. 2011; Aon et al. 2004; Bach et al. 2003; Rambold et al. 2011; Santel et al. 2003; Sukhorukov et al. 2012), there is less understanding of the interaction between intracellular structures, such as the cell’s cytoskeleton (Anesti and Scorrano 2006) and external mechanical parameters, such as the cell stretching (Ali et al. 2004) and mitochondria, which influences subsequently mitochondrial functions. As the impacts of stretching of the cells are predominantly transduced by the cytoskeletal scaffold to which mitochondria are connected, it is imaginable that the organization of the cytoskeleton and mechanical features commonly interfere with the structure and function of the mitochondrial framework. The main objective is to give an overview of two relevant regulatory determinants of mitochondria, the intracellular cytoskeleton and the external mechanical parameters. An essential mechanical element is the deformation to which the cell in the body is subjected during its normal tissue function. The in vivo natural dynamic character of the stretch pattern has recently demonstrated to be beneficial in sustaining the general mitochondrial function (Bartolak-Suki et al. 2015). Meanwhile, the mechanical stiffness (roughly defined as the response of the extracellular matrix under stress to a unit alteration in the strain of the specimen) has proven to be an important regulator of multiple functions of the cell (Discher et al. 2005). Thereby, the important question of whether the extracellular matrix mechanics additionally impact the function and structure of mitochondria should be answered. In the following, the morphological features of the mitochondria are presented briefly and with respect to their impact on mechanical properties and a special emphasis on the distinct function guided by the special configuration of the framework. In specific detail, the interaction of mitochondria and the structural architecture of the cytoskeleton is pointed out with a focus on the gained potential of the interaction of the two structures. It is presented how the stretching and the dynamical remodeling of mitochondrial patterns alters their overall structure and function. In the end, it is provided how the stiffness of the surrounding extracellular matrix governs the shape of the mitochondria and the generation of energy in form of ATP. Finally, it is discussed which general

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functional role mitochondria fulfill in mechanobiology and how mitochondrial mechanosensitivity leads to the emergence of several diseases and the aging process.

6.2

How Are Mitochondrial Structure and Production of ATP Related?

At first glance, it is assumed that the mitochondria emanate from free-living alpha-proteobacteria that evolved a symbiotic association with the target host cell (Margulis 1975). However, there is compelling phylogenetic evidentiary proof now that this scenario exists (Wang and Wu 2015), substantiated in that mitochondria possess their very own DNA, designated by mtDNA, and that multiple mitochondrial proteins originate from bacteria (Gray 2015). These organelles are bound by an outer membrane and an inner membrane, in a similar way as in bacteria. The outer membrane enables the metabolite transfer between the inner membrane and the cytosol, while also preventing mitochondria from releasing pollutants such as ROS and mtDNA into the cytosol (Zhang et al. 2010; Pernas and Scorrano 2016). Nonetheless, mitochondrial ROS serves in subtoxic quantities as post-release signal molecules in the cytosol (Suzuki et al. 1997). The inner membrane of mitochondria is composed of specific morphological sites which encompass the boundary region to the membrane boundary, the cristae and the junctions of cristae (Vogel et al. 2006). The cristae represent the invagination of the inner membrane of mitochondria, which pronouncedly enlarges the internal surface of mitochondria. The proteins providing the electron transport chain are targeted to this surface. Mitofilins can contribute to the organization of the morphology of cristae and can be accumulated between inner and outer membranes of mitochondria (John et al. 2005). The luminal side of the membrane harbors the mitochondrial matrix, an which the Krebs cycle is active. In specific detail, the Krebs cycle delivers NADH and FAD into two transmembrane complexes, such as the respiratory complex I and II. Energetic electrons can travel through the respiration complexes of the electron transport chain, whereas protons are transported from the matrix into the intermembrane space, resulting in a formation of charge and proton gradient over the inner membrane that is termed the electromotive force and functions similar to a battery storing electrochemical energy. The final component of the electron transport chain is the ATP synthase that utilizes the electromotive force to add an inorganic phosphate group to ADP which results in ATP. This process is termed the oxidative phosphorylation. Thereby a part of the ATP amount is employed by the mitochondria and the remaining ATP is secreted into the cytoplasm, which can be employed as chemical energy for multiple processes inside the cell (Fig. 6.1).

6.3

Cluster Formation of Mitochondria

Mitochondria seem to be rather isolated organelles that take food from their surrounding environment, such as the cytoplasm. In addition, mitochondria continuously undergo highly dynamically remodeling and perform interactions with other organelles (Pernas and Scorrano 2016). At the luminal side of the mitochondria are the cristae located that are able to pronouncedly restructure themselves due to environmental factors and mechanical stresses or biochemical alterations. For instance, the supply with energy-rich substances alters the entire architecture of the mitochondrial cristae (Patten et al. 2014) that in turn can promote the assembly of the respiratory complex and subsequently trigger the overall growth of the cell dependent on the proper function of the mitochondria (Cogliati et al. 2013). The dimerized form of the ATP synthase can fall into two parts due to cell aging, which is coupled to a loss of the cristae invagination (Daum et al. 2013) that leads to the hypothesis that the ATP synthase activity and the structure of cristae are closely coupled. Specifically, the dynamin-related GTPase

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Fig. 6.1 Mitochondria represent the powerhouses of the cell. A sketch of a mitochondrion displays its outer and inner membrane, the cristae and the matrix. Fat and sugar can enter the mitochondria through specific channels of the outer membrane (left). The citric acid cycle can feed the respiratory complex chain and thereby generates an electrical and proton (H+) gradient, which serves as an electromotive force across the inner membrane. The ATP synthetase employs these electromotive forces to produce ATP from ADP and inorganic phosphate (Pi) (right)

protein Optic Atrophy 1 (OPA1) has been identified as the primary regulator of the maintenance of the cristae. OPA1 seems to fulfill specific tasks in the fusion of mitochondria and in the remodeling process of cristae, which is properly most recognized in the programmed cell death, referred to as apoptosis (Frezza et al. 2006). Cluster of mitochondria possesses the capacity to fuse with other single clusters of mitochondria to create a large organelle. Subsequently, these mitochondrial clusters assemble a dynamically reticular network containing multiple interconnections that are distributed through the whole cellular volume. The elements of the mitochondrial network elements possess a cylindrical shape that have a diameter of about several hundred nanometers. The formation of the mitochondrial network by fusion requires the combination of the outer and inner membranes of two distinct mitochondrial clusters, and their constituents, together with mtDNA, are thoroughly intermixed over 12 h (Legros et al. 2002). The fusion of the outer membrane of mitochondria is guided by the fusion proteins, such as the mitofusin1 (Mfn1) and mitofusion2 (Mfn2) (Chen et al. 2003). However, the fusion of the inner membrane of mitochondria is guided by the OPA1 protein (Olichon et al. 2003). Specifically, while fusion of outer

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membranes occurs regardless of oxidative phosphorylation, inner membrane fusion necessitates enzymatic cleavage of OPA1 induced by high membrane potential (Mishra et al. 2014), implying only healthy and active mitochondria are able to fuse properly. The large mitochondrial clusters are found to split into two or several more parts (Fig. 6.2). After this process of mitochondrial cleavage, the smaller mitochondrial clusters have an almost spherical shape with diameters of several hundred nanometers. The fragmentation of mitochondria is necessary for their orderly distribution of mitochondria during the division of cells and the growth of embryos (Yaffe 1999). However, if cleavage is not managed and compensated by fusion, the network becomes overfragmented, resulting in glucose break-down, mitochondrial internal membrane potential decrease and consequently reduced ATP production (Bach et al. 2003). The process of mitochondrial cleavage appears to be strictly controlled by a combined sequence of events to which cytoplasmic components, cytoskeletal elements and organelles all participate in three steps: firstly, labeling of the cleavage site; secondly, building the cytosolic dynamin-related protein 1 (DRP1) into a superstructure at the cleavage site (Fig. 6.3); and thirdly, narrowing the membranes at the cleavage site to divide the mitochondrial cluster into subsidiary clusters (Pernas and Scorrano 2016; Hoppins et al. 2007). The cleavage and fusion rates of mitochondria seem to be strongly kept in a balance (Lackner 2014). In specific detail, the association of two heptad-repeat regions of Mfn2 to DRP1 leads to the fusion of mitochondria, while the association of these two regions of Mfn2 impairs the fusion process (Huang et al. 2011). To be more even specific, the long version of OPA1 is able to induce the fusion of mitochondria, whereas the short version of OPA1, which has been enzymatically cleaved, supports the opposite behavior of mitochondria, as it causes the fission of mitochondria (Anand et al. 2014). Moreover, the short version of OPA1 has been detected to be co-localizes with the interaction sites of the mitochondria with the endoplasmic reticulum (ER). Consequently, the cleavage of mitochondria

Fig. 6.2 Intracellular and extracellular events contribute to the mitochondrial architecture and function. Intracellular mitochondrial dynamics include processes, such as fusion, fission, mitophagy and biogenesis. The gray colored mitochondrion is damaged and undergoes degradation by mitophagy. The green lines indicate the site of microtubules, on which small mitochondria can migrate along with the help of motor proteins. The dashed line indicates the site of mitochondrial fission. Cells are coupled to the extracellular matrix and are subject of external mechanical forces, which can trigger fusion and fission of mitochondria

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Fig. 6.3 Drp1/Dnm1-dependent (first way) and -independent (second way) mitophagy. The first way facilitates the mitochondrial division through the production of a mitochondrial fragment that is enclosed by an isolation membrane and later on it is engulfed in an autophagosome. The second way involves the autophagosome biogenesis through the initiation of mitochondrial tubules and creates a budding event and finally the full division of a mitochondrial fragment, which is enveloped by an isolation membrane. The autophagy mechanism generates the scission and closure of the isolation membrane to generate the autophagosome

is facilitated by the interactions between mitochondria and relatively narrow ER tubules. Aquaporindriven water channels provide the swelling of ER tubules, and hence, the constriction of the tubules bears the mechanical force needed for the cleavage of mitochondria (Lee et al. 2013). In fact, the mitochondrial fission can take place directly at the interaction region of mitochondria with ER tubules, where the constriction is triggered before DRP1 is recruited to the cleavage site (Friedman et al. 2011). Additional functions of the tethering between the ER and mitochondria have been reported (Lackner 2014). Among them are at least two processes providing the complete dynamics of mitochondria: firstly, the biogenesis that drives the generation of mitochondrial content through the nucleus and the mitochondria and secondly, the mitophagy that degrades and subsequently phagocytized male-functioned mitochondria.

6.4

Properties of Mitochondrial Networks

Due to the highly organized and complex architecture of mitochondrial clusters, fluorescence microscopy and mathematical analyses have been employed to analyze this structural network. The fission and fusion processes seem to be not fully independent of one another, and consequently, they seem to create a cycle (Fig. 6.4), where the probability that a fusion succeeds the fission and a fission joins the fusion is approximately 0.8 (Cagalinec et al. 2013). In neuronal cells, it was found that cleavage is controlled by the length of the mitochondrial cluster, while fusion is managed by cluster motility to ensure that the total rates of cleavage and fusion are achieved and preserved in a homeostatic state (Cagalinec et al. 2013). The exact rates and probabilities for a fusion or fission event depend on the specific cell type. The energy state of mitochondria is crucially connected to their structure because they dissipate the inner membrane potential, inhibit complex III and complex V

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Fig. 6.4 Mitochondrial stress response is coordinated with the shape of mitochondria. Multiple different mitochondrial pathways react to stress or damage (loss of mitochondrial membrane potential and subsequently loss of protein import capabilities) and are connected to mitochondrial dynamics. During unfolded protein stress response (UPRmt) pathway, loss of import leads to accumulation of transcription factor ATFS1 in the nucleus, which triggers the mitochondrial repair mechanisms and the metabolic adaption reaction. Loss of membrane potential induces OMA-1 dependent proteolysis of long isoforms of the membrane fusion DRP OPA1, which leads to mitochondrial fusion and potentially elevates ER-mediated division (ERMD) that results in mitochondrial fragmentation accumulating PINK1 kinase and hence induces mitophagy. In addition, ERMD domain may change to directly induce BAX oligomerization on the outer mitochondrial membrane, which in turn permeabilizes the membrane and releases cytochrome c that causes cell deaths. Mitochondrial dysfunction and stress can initiate the mitochondrial hyperfusion depending on the mitochondrial membrane potential and the presence of long and short OPA1 isoforms. Hyperfused mitochondria circumvent mitophagy and buffer dysfunctions

(ATP synthase) of the electron transport chain, or inhibit cytosolic ATP by limiting glycolysis. All of which causes a decreased rate of fissions and fusions, diminishes their motility, which is determined by the diffusion coefficient analysis, decreases the likelihood of cluster movements bursting and increases the number of fragment of mitochondria (Giedt et al. 2012). In order to reveal the precise structure of individual clusters, the length of the cluster backbone can be determined (Cagalinec et al. 2013) and the apparent aspect ratio (AR), which is the ratio of major to minor axis of an ellipse fit to the mitochondrial cluster shape, or the form factor (FF), which presents the perimeter squared divided by the product of 4p and the area, can be calculated (Koopman et al. 2005a, b). More precisely, the AR reveals how spherical the shape of the cluster is, while the FF correlates to the degree of branching across the backbone of the cluster. The total mitochondrial complexity of the network is expressed as the fractal dimension Df, which refers to the network’s room filling capacity (Palmer et al. 2011; Bartolak-Suki et al. 2015). When in 2D approaches, the factor Df almost 1, the network is mainly composed of lines, and when Df is nearly 2, the network is more of a dense 2D object just like a filled circle. In order to investigate what is responsible for the structural properties of the mitochondrial network, it is appropriate to display some fluorescent labels linked to the mitochondria and to calculate the aforementioned indices before and after the administration of various stimuli or inhibitors. For instance, the mitochondrial average cluster size in vascular smooth muscle cells (VSMCs) in culture free of stretching appears to depend on a number of

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inhibitors, including blebbistatin, an inhibitor of non-muscular myosin II, dynasore, an inhibitor of DRP1, and paprotrain, an inhibitor of mitotic kinesin-like protein 2 that permits mitochondria to travel down microtubules (Bartolak-Suki et al. 2015). In extension, the chronic suppression of complex I with rotenone in human skin fibroblasts as a model of mitochondrial dysfunction resulted in a marked elevation of FF, which points out to the hyperproduction of ROS leading to mitochondrial protuberances (Koopman et al. 2005a, b). An overall view of the entire mitochondrial network shows that this network spreads efficiently across the cell, probably an evolutionary outcome of the ideal spatial distribution of energy delivery in the guise of ATP. One of the features of this overall network is its connectivity by means of percolation theory (Stauffer and Aharony 1992). Examine a grid in which each neighboring location is covered with the probability p. A cluster can be defined as a group of connected occupied sites. When p rises from 0 to 1 and specifies a completely empty and separate grid, the size of the clusters gradually increases. There is a stage where a large cluster crosses the grid and offers full interconnectivity from one end to the other. The transition from a separate grid to a grid containing a connected cluster covering the entire system takes place when p exceeds a critical percolation threshold, which is characterized by pc. The microstructure of the percolation pattern at p = pc is a self-similar fractal. A percolation transition of this type can therefore be expected when a critical mass density of mitochondria is fused to a global network. In order to test this concept, in isolated ventricular myocytes, it was shown (Aon et al. 2004) that the mitochondria build a network close to the critical point and that the fractal properties of the network match with a percolating mitochondrial network perfectly. The question of how signals are sent over such a network is even more challenging. When mitochondria aggregate levels of ROS that are above a certain threshold, a small additional release of ROS generated locally by electron transport chain leakage initiates a ROS wave propagating through the interconnected cluster, depolarizing initially the inner membrane potential and subsequently switching to oscillations. The biological implications include that a shift to oscillatory patterns may destabilize the heart. Precisely, it means that the potential for action of repolarization in the entire heart, implying that there are microscopic levels of criticality, can be converted into the organism’s death on the microscopic length scale (Aon et al. 2004). Moreover, this scenario leads to the hypothesis that mitochondria have the potential to signalize across cells and reach the tissue level to interfere with the fate of organs and the whole organism.

6.5

Interactions Between the Cytoskeleton and Mitochondria

Besides the structure, dynamics and regulation of mitochondria, there exists an interplay between mitochondria and the cytoskeleton. A function of the cytoskeleton, which is a filamentous protein scaffold, is to prevent deformation of the entire cell in order to resist external forces and allow shape alterations during cell motility, the transport of cell cargo, such as mitochondria. Moreover, the cytoskeleton signals in mechanotransduction processes through the conversion of mechanical signals into biochemical signals (Fletcher and Mullins 2010). In turn besides transducing signals, such as physical forces, from the outside to the inside of the cell, the cytoskeleton can transmit forces from organelles, such as the nucleus and the mitochondria to the outward environment or to other neighboring cells. The cytoskeletal network is able to react to external forces by displaying hysteresis and memory, and long-lived cytoskeletal structures are epigenetically passed on to future generations after cell division (Fletcher and Mullins 2010). Due to this fundamental mechanical function of the cytoskeleton in general cell behavior, the focus will be on the mechanical issues of the interface between the cytoskeleton and mitochondrial clusters.

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The cytoskeleton consists of three major types of biopolymers: actin filaments synonymously referred to microfilaments, intermediate filaments and microtubules. Based on the connection via cross-linkers, stabilizers and motor proteins, these filaments can assemble interconnected architectures. The shape of cells and their mechanical phenotype depends on the amount of the structural organization and the predominant network type. These dynamical remodeling scaffolds help the cells to adapt their structural resistance to the amount of externally applied forces toward the cell and support the communication of the cell with other adjacent cells or multiple cell organelles by the generation and transmission of cell mechanical forces. The structural reorganization of the cell’s cytoskeleton usually requires the polymerization and depolymerization of filaments that is driven specific factors, including nucleation promoting factors supporting the initial growth of filaments, filament capping proteins, impairing the further polymerization process, polymerases, increasing the polymer elongation and depolymerizing factors that disrupt filaments and networks through promoting the disassembly (Fletcher and Mullins 2010). Consequently, all three major cytoskeletal components are linked to several mitochondrial functions.

6.5.1 Interactions Between Mitochondria and the Actin Cytoskeleton Initially, an early study proposed a spatial co-localization of gamma-actin with mitochondria of skeletal muscle cells (Pardo et al. 1983), while a later trial revealed that mitochondria in sympathetic neurons of chicks can move along the axon in bilateral directions and their movement demands either microtubules or actin, depending on which cytoskeletal network is available (Morris and Hollenbeck 1995). It was nevertheless later observed that in axons and dendrites, the mitochondria exhibited a preferential movement across the microtubules, even though a restricted movement across the actin remained feasible (Ligon and Steward 2000). While the movement of mitochondria necessitates actin in plants and fungi or microtubules in mammalian cells, actin assists in the immobilization of mitochondria in neurons at sites where ATP is demanded (Boldogh and Pon 2006), by the reinforcing its Ca2+-dependent interplay with actin (Kremneva et al. 2013). Actin contributes to the rearrangement of the mitochondrial system during mitosis. The delivery of mitochondria to the daughter cell at the terminal stage of mitosis is facilitated by the cell cycle-dependent enrollment of Cenp-F, a cytoskeleton-associated protein, by a mitochondrial protein known as Miro (Kanfer et al. 2015). It has been revealed that the cortical actin structure relies on the presence of ATP and non-muscle myosin II, and consequently, the degree of the cross-linking of the actin network by myosin II correlates with the overall stiffness of the cell (Parameswaran et al. 2014). The inhibition of nonmuscular myosin II alters cortical actin (Bondzie et al. 2016); however, this inhibition decreases the average mitochondrial cluster size in VSMCs (Bartolak-Suki et al. 2015). In turn, mitochondria are able to affect multiple actin-based cell functions. For instance, as the production of ATP depends mostly on the mitochondria, the entire cell stiffness seems to rely additionally on mitochondrial production of ATP. The same factor seems to account for the cell contraction behavior, since ATP is highly needed for the myosin-based cell contraction. In fact, in VSMCs and in aorta rings, it has been revealed that the impairment of the ATP synthase by oligomycin causes a decrease in the active generation of forces (Bartolak-Suki et al. 2015). Consequently, there appears to be a subtle and double-directional association between cortical actin and mitochondrial architecture leading to general cell-mechanical functions such as stiffness and contractility.

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6.5.2 Can Microtubules Impact the Function of Mitochondria? For over four decades, it is known that mitochondria strongly associate with microtubules in multiple cell types (Heggeness et al. 1978). Both alpha and beta tubulins can be targeted to the lumen of mitochondria, and they are closely connected to mitochondrial voltage-dependent anion channels (Carre et al. 2002). The close storage of beta-tubulin II in the neighborhood of voltage-dependent anion channels enables it to facilitate the transition of the mitochondrial permeability (Kuznetsov et al. 2013), where the pores, which are mainly voltage-dependent anion channels, located on the outer membrane open and subsequently cause their necrosis and apoptosis (Kim et al. 2003). Microtubule filaments act as train rails on which mitochondrial clusters move inside the cell, using motor proteins such as dyneins and kinesins that move to the minus or plus ends of the microtubules (Vale 2003). The disassembly of microtubules fully removes the mitochondrial motility (Ligon and Steward 2000), which is required for fusion and cleavage and for the maintenance of a healthy mitochondrial microstructure and for cellular bioenergetics. Theoretical analysis and mathematical modeling yield empirical results proving that, in addition to fusion and cleavage rates, the mitochondrial structure is determined by the retrograde and anterograde motions and the equilibrium between these rates is responsible for the heterogeneous dispersion of mitochondria throughout the cell (Sukhorukov and Meyer-Hermann 2015). Microtubules are also responsible for cell shape and stability through their ability to absorb compressive forces (Stamenovic et al. 2002). External forces are capable of changing cell shape, leading to a reorganization of microtubules; effectively, cyclic uniaxial stretching has affected cell orientation and microtubular microstructure (Morioka et al. 2011), which may influence mitochondrial cluster and system properties and consequently ATP generation.

6.5.3 How Can Intermediate Filaments Affect Function and Structure of Mitochondria? Apart from mitochondria, there are indications that intermediate filaments play a role in mitochondrial architecture and function (Anesti and Scorrano 2006). In this way, for instance, plectin, a cytoskeletal cross-linker, is physically affiliated to desmin, an intermediate filament that is in orderly fashion associated with mitochondria along the length of the sarcomere of striped muscles, pointing to the possibility that these proteins support the branching habit of mitochondria (Reipert et al. 1999). Vimentin represents another intermediate filament linked to mitochondria, as vimentin null cells demonstrated mitochondrial fission and reorganization, possibly by altering the connection between mitochondria and microtubules (Tang et al. 2008). Vimentin is involved in cell mechanics and safeguards the cell against compressive stress (Mendez et al. 2014), indicating the feasibility of external mechanical stress controlling mitochondrial architecture and function.

6.6

Mechanobiological Aspects of the Structure and Function of Mitochondria

Almost all cell types in the human body experience mechanical forces, including external pressure, shear stress, tensile stress and the environmental stiffness of the local extracellular matrix network. These mechanical parameters impact cell functions through the coupling of cell surface receptors including integrins at focal adhesions that represent interaction regions with the surrounding extracellular matrix, which is composed of several fibers, mostly collagen type I with the conserved-binding motif Arg-Gly-Asp (RGD). Cells are constantly exposed to the extracellular matrix, and hence, they

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can adapt to the matrix itself and react to such specific stimuli. Consequently, a cell tries to continuously reach and maintain a homeostatic balance with the extracellular matrix. In fact, vascular endothelial cells can mechanically sense forces, such as the shear stress in blood or lymph vessels that is evoked by the blood flow (James et al. 1995). In contrast, epithelial kidney cilia cells (Praetorius et al. 2005), bone cells including osteoblasts, osteocytes and osteoclasts (Jacobs et al. 2010), cartilage cells, such as chondrocytes (Shao et al. 2012), and eye (Luo et al. 2014) cells, such as cone or rod cells, can react to pressure in their specific tissues. The contraction of muscles exerts stresses on the muscle cells (Martineau and Gardiner 2001) and on adjacent nerve cells (Tock et al. 2002). The cyclic alteration of the blood pressure causes in the arteries circumferential stresses that are probing the vessel cells, such as VSMCs (Osol 1995). The process of breathing exerts also cyclically stretches toward all lung cells (Waters et al. 2002). With regard to the stiffness of the extracellular matrix, stem cells are a good candidate for highly adaptive cells because, when placed on soft extracellular matrices, which should resemble the neurogenic mechanical properties of the brain, they exhibit a soft phenotype. When the stem cells are placed on stiffer extracellular matrices, which resemble the mechanical phenotype of myogenic cells, they display a stiffer mechanical phenotype. Consequently, when the stem cells are cultured on even very stiff osteogenic extracellular matrices, which represent the mechanical phenotype of the muscle, they increase their stiffness (Engler et al. 2006).

6.7

Impact of Transient and Monotonous Stretching

Due to the fact that the cytoskeleton is the primary supporting element that reacts to all external mechanical impulses, the strong connection between the cytoskeletal and mitochondrial systems implies that mitochondria also need to be mechanosensitive. Despite this, mitochondrial reactions to mechanical irritations were only taken into account in a relatively short time. In an in vitro enhanced stretch model (24 h with 20% surface loading) of an abnormal mechanical environment, cardiomyocytes were subjected to apoptosis as cytochrome c was liberated out of mitochondria (Liao et al. 2004). The mitochondrial membrane potential also decreased, which apparently led to mitochondrial splitting (Liao et al. 2004). Bcl-2 proteins lead to stretch-facilitated mitochondrial apoptosis. The breathing muscle vulnerability of the intensive care unit due to compromised membrane contractility was studied with a five-day mechanical ventilation model in farrows (Fredriksson et al. 2005). Whereas the mitochondrial amount did not vary, the electron transport chain complex IV activity dropped by 21%. Mechanical respiration, by contrast, absorbs the inherent flexibility of conventional tidal respiration and therefore indicates strongly that long-lasting abnormal monotonous mechanical impulses lead to distinct molecular alterations in the mitochondria. Periodic mechanical loading enhanced ROS synthesis in endothelial cells in an actin cytoskeleton-dependent way (Sukhorukov et al. 2012). In the same direction, a cyclic extension of lung epithelial cell types increased the expression of ROS in a manner dependent on duration and amplitude, indicating that lung hyperdistension during mechanical respiration can cause mitochondrial ROS-based lung injury (Chapman et al. 2005). Specifically, the trial also provided direct proof based on imaging that a total equibiaxial strain, with a 17% strain on the elastic membrane where the cells adhered to, led to local mitochondrial strain with up to 32% linear strain. When pulmonary fibroblasts were subjected to large transient equibiaxial extensions of up to 30%, mitochondria were torn at distinct sites instantly after extension (Imsirovic et al. 2015). Cells can be stained with the dye tetramethylrhodamine methyl ester (TMRM), whose intensity is regulated by the inner mitochondrial membrane potential and consequently by the production of ATP (Kadenbach et al. 2010) and thus ultimately represents a reliable mitochondrial marker (Ehrenberg et al. 1988). The findings indicate that external mechanical stresses can initiate cleavage directly and instantaneously. The culprit is that the cytoskeleton remains in a pre-

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stressed condition with tensile forces on actin fibers as a result of myosin motor activity (Ingber 2006), predisposing the cytoskeleton for the fast transfer of mechanical forces over long distances. In actual fact, it reminds of the quick and far-reaching mechanical transmission of force from focal adhesions to the nucleus (Hu et al. 2005). Although it has been confirmed that external mechanical forces, both temporary and long-term monotonic, affect the morphology and function of mitochondria, the internal mechanical microenvironment has also been found to propel mitochondrial cleavage via elastocapillary instability (Gonzalez-Rodriguez et al. 2015).

6.8

Fluctuations Affect Mechanotransduction Behavior

Tissues and cells of the body can react to external mechanical stresses by variations and fluctuations of their cellular constituents and structures. Beat-by-beat blood pressure alterations display significant variability, which is increased with hypertension (Mancia et al. 2001; Schillaci et al. 2012). Breathing produces a large breath-to-breath flexibility of the lateral volume (Dellaca et al. 2015). As the quantities and timing of mechanical stresses both govern the response of the actual cellular signal (Hoffman et al. 2011), fluctuations in mechanical impulses are anticipated to interfere with the modalities of mechanotransduction, a process referred to as fluctuation-driven mechanotransduction (Suki et al. 2016). Mechanotransduction is a mechanism that can be integrated into all mechanosensitive cellular processes over the course of evolutionary time scales. Under standard laboratory conditions, however, mechanotransduction is examined with either static or cyclic monotonic strain. It has been reported in several recent studies that fluctuations in cyclic loading or shear stress, referred to as variable strain or shear stress, profoundly affect cell function, including cytoskeletal organization, bioenergetics and transmission of signals (Bartolak-Suki et al. 2015; Arold et al. 2009; Uzarski et al. 2013). A recent report has demonstrated that variation-induced mechanotransduction influences mitochondrial morphology and functional properties directly (Bartolák-Suki et al. 2017). In concrete terms, the ATP production rate determined by the quantitative analysis with a tetramethylrhodamine methyl ester labeling in mitochondria, which is a dye, whose intensity is correlated with the inner mitochondrial membrane potential and subsequently with the production of ATP, was twice as high for VSMCs grown on elastic membranes and equibiaxially elongated as for monotonic elongation after four hours of variable elongation (Bartolak-Suki et al. 2015). In addition, variable strain also directly involved constituents and phosphorylation of electron transport chain complexes: ATP synthase and cytochrome c oxidase and their phosphorylated form together with Mfn1 and Mfn2 were elevated, however not DRP1. Remarkably, variable strain has also initiated mitochondrial biogenesis, as the master regulator of biogenesis stimulated by external factors, the peroxisome proliferatoractivated c coactivator (Wu et al. 1999), PGC-1a, has risen relative to monotonic strain (BartolakSuki et al. 2015). These biochemical alterations were characterized by various structural shifts, such as an enhanced organization of actin, microtubule and mitochondrial meshes, which are distinguished by their fractal dimension and by their coefficient of variation (Bartolak-Suki et al. 2015). To investigate the potential mechanisms of fluctuation-driven mechanotransduction, inhibitory pharmacological drugs of the polymerization of actin, the depolymerization of microtubules, the ATP synthase, focal adhesion kinase (FAK), or the presence of calcium were employed. Both ATP synthesis and the size of the mitochondrial cluster were reduced, whereas the variable stretch can still keep an increased membrane potential of mitochondria than those stretched monotonously. Inhibitors of the microtubule and vimentin assembly caused the abolishment of the membrane potential of mitochondria and thereby differences between monotonously stretched and variable stretched cells vanished. In the case, when non-muscular myosin II, DRP1, which regulates the cleavage of

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mitochondria (Otera et al. 2013), or the mitotic kinesin-like protein 2, lowered the membrane potential in variably stretched cells, it enters the membrane potential levels of monotonically stretched cells. Consequently, the functional impact of fluctuation-driven, mechanotransduction-induced ATP generation during myosin light-chain phosphorylation in VSMCs in culture and aorta rings was elevated, resulting in turn in an increased contractile force generation during variable stretch in aortic rings (Bartolak-Suki et al. 2015). Thus, VSMCs are able to exploit fluctuations in their mechanical surroundings, and the extracted excess energy becomes apparent in enhanced chemical energy stored in ATP as a consequence of the reorganization and interaction of cytoskeletal and mitochondrial systems. The structure complexity-function relationship is a result of macroscopic mechanical fluctuations that alter the complexity of the cytoskeletal and mitochondrial framework, control oxidative phosphorylation and improve bioenergetics. The network complexity and the production of ATP of unstretched, monotonous stretched and variable stretched cells need to be compared to reveal similarities or dissimilarities. To shed light on this, unstretched cells generate and require the smallest amount of ATP, whereas variably stretched cells generate and require themselves the largest amount of ATP. Therefore, unstretched cells are nearest, while variable stretched cells are most distant from thermodynamic equilibrium as they are able to use energy from microenvironmental fluctuations to load mitochondria, the battery of life. The far from equilibrium operation is facilitated by a higher complexity of the mitochondrial system architecture. But it remains to wait and see whether the fluctuation-induced mechanotransduction has comparable results in certain other cell types.

6.9

Role of the Stiffness of the Extracellular Matrix on Mitochondria

There is not much knowledge about the regulatory function of the extracellular matrix stiffness on the structure and function of mitochondria. An evidence was supplied by an experiment in cardiac myocytes, basic metabolism is manipulated by extracellular matrix stiffness and, even bolder, the capacity of cells to adjust to metabolic stress is governed by both extracellular matrix stiffness and orientation of fibers (referred to as fiber alignment (Lyra-Leite et al. 2017, 2019). In order to finish these investigations, the hypothesis is that there exists a linkage between the mitochondrial structure and function of VSMCs and the stiffness of the extracellular matrix scaffold, since there has been similar evidence for this linkage in cardiac myocytes. The influence of the substrate stiffness on the structure and function of mitochondria has been determined employing elastic gel (synonymously termed NuSil® 8100) with tunable stiffnesses due to the alteration of the ratios of the polymer and its cross-linker (Yoshie et al. 2018). Hence, several ratios of the polymer to cross-linker, such as 1:1, 1:2 and 1:5, have been analyzed after they have been incubated at 70 °C for one day. Stiffer gels can be generated by the addition of silicone elastomer Sylgard 184, which is mixed at a ratio of 1:10 to the 1:1 NuSil at 20 and 33% by weight. The stiffness of the gels was revealed by employing a uniaxial stretch (Araujo et al. 2011). The size of the gel was measured before fixing in a stretching device that applied a known displacement and recorded the corresponding force. Stress and strain were obtained using the measured force and physical dimensions of the specimen, and the slope of a linear fit was used as the stiffness. In the next step, new gel layers were polymerized and ligated using collagen type I. VSMC derived from bovine thoracic aortae was placed on top of these gels that possess stiffnesses in a range of 1–75 kPa. Thereafter, the cell’s mitochondria were stained by using the membrane potential marker tetramethylrhodamine. In fact, the effects of stiffness on the size of the mitochondrial cluster were major, although the maximal stiffness difference was 18% for cells seeded on 12.5 kPa to all other stiffness values examined, such as 1.0, 12.5, 25 and 75 kPa. The stiffness impact on the tetramethylrhodamine intensity was even detected to be more pronounced, where the maximum intensity has been identified at 12.5 kPa with a

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27% difference compared to the other values. Moreover, it should be mentioned that a stiffness of 12.5 kPa is highly related to the in vivo stiffness of the vascular wall, on which these cells are adhered to (Sehgel et al. 2015). Subsequently, the entire structural organization and the proper function of mitochondria within VSMCs seems to be adapted to this optimal stiffness value of the extracellular matrix stiffness creating the local vascular wall.

6.10

How Contributes the Mitochondrial Structure and Function to Diseases and Aging?

A focus lies on the linkage between the structure and function of mitochondria and the cytoskeleton and the impact on mechanical features on the cell body. It seems to be obvious that the fission dynamics and the fusion events fulfill a crucial role in the entire organization of the mitochondrial network structure that in turn controls the intramitochondrial processes including the generation of ATP and even external mitochondrial processes including apoptosis can be regulated. In specific detail, mitochondria can be seen as a network that seems to be critically connected to multiple cytoskeletal filamentous scaffolds, such as actin filaments. As the cell’s cytoskeleton is known to be associated with all mechanosensitive functions, it seems to be possible that the entire mitochondrial scaffold as an organelle fulfills the same mechanosensitive tasks as the cytoskeleton, and hence, it should be treated as an integral section of the entire mechanosensing machine of the cell. In addition, the mitochondrial scaffold seems to be associated with the bidirectional mechanotransduction. In the first place, the network reacts to elongation and its time fluctuations applied to mitochondria on the cell scale by modifying biochemical signaling to generate ATP and ROS on a scale considerably lower than that of the network as a whole. But on the opposite, the mitochondrial system is able to control whole cell and possibly tissue and organ processes, including tissue contraction, apoptosis and organ-based male function, by regulating the cytosolic availability of ATP, ROS and cytochrome. Mitochondria are associated with a wide spectrum of diseases and processes of cell aging (Zhang et al. 2010; Olichon et al. 2003; Kim et al. 2003; Bonnet et al. 2006; Bratic and Larsson 2013; Chistiakov et al. 2014; Gomes et al. 2013; Irwin et al. 2003; Mora et al. 2017; Nunnari and Suomalainen 2012; Reddy 2011; Sutendra et al. 2011; Zhou et al. 2012). In multiple diseases, mechanical features play a key or a prominent role; therefore, it seems possible that this is a reason why the function and the structure of mitochondria are found abnormal in many diseases when the mechanical properties of the extracellular matrix environment also become also abnormal. In fibrosis, the stiffness of the extracellular matrix rises, which in return modifies the cytoskeleton and therefore induces a mitochondrial reaction (Mora et al. 2017). A feature of asthma is the pathologically very strong contractional capability of airway smooth muscle cells. As the contraction of the muscle is known to require ATP and the extracellular matrix stiffness of the airway wall additionally needs to be elevated in asthma based on the redistribution of the tissue, mitochondria seem to be associated with the initiation and the further development of asthma (Reddy 2011). In the same way, the physiological process of aging involves a stiffening of the vascular wall (Roccabianca et al. 2014), and also the stiffness of the vascular wall (Mitchell et al. 2007) and similarly the variability in blood pressure (Mancia et al. 2007) are both elevated hypertension. Each of which factors seem to affect the mitochondrial processes, and thus, mechanism for the alteration of mitochondria seems to be novel in promoting different processes of aging and hypertension. Moreover, it can be hypothesized that almost all other body cells are subject to mechanical perturbation, and hence, the mitochondria of them may be associated with diseases where mechanical features are impacted. At present, the importance of mitochondrial structure and function in mechanobiology is underestimated, while mechanotransduction certainly lacked any influence on medicine. In the future, it requires to be

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examined that mitochondria and the mechanobiology are closely linked together and insights in this linkage may help to find new pathways for the treatment of several human diseases.

6.11

Mechanical Force Evokes Mitochondrial Fission

It is the present knowledge that eukaryotic cells are densely filled complexes that are assembled by macromolecules and entangled organelles, which are moved continuously through the cytoplasm and undergo ongoing reshaping. An interesting feature of organelles is that they try not to be entangled with each other or smashed against one another due to the constrained space. Apart from staying single, mitochondria can assemble complicated networks that are under continuous restructuring by division (synonymously termed fission) and fusion of mitochondria. It has been demonstrated that the fusion of mitochondria is induced by mechanical forces acting on them. The mechanical probing of mitochondria, which are evoked by migrating intracellular pathogens, can be mimicked by exertion of external pressure using an AFM or the migration of the entire cells over uneven microsurfaces. All of which leads finally to the involvement of the fission mechanism of mitochondria and their subsequent division. It seems plausible that the mitochondrial fission factor, due to its affinity for narrow mitochondria, serves as a membrane-bound force sensor to engage the fission apparatus in mechanically loaded places (Helle et al. 2017). Therefore, mitochondria conform to their environment by perceiving and reacting to biomechanical signals. The results that mechanical events can be connected with biochemical reactions in membrane dynamics allow to elucidate how organelles in the overfilled cytoplasm live in an ordered way together (Helle et al. 2017). Eukaryotic cells are tightly filled with macromolecular complexes and entangled membranous organelles (Marsh et al. 2001). Some organelles, such as ER and mitochondria, gather to form highly intricate and dynamic interconnections and increase the complexity of the microcellular architecture. In addition, the cells are continuously reconfiguring their cytoplasm. In this way, for example, the vesicular and membrane-like turnover constantly travels large assemblies and organelles over long distances throughout the cytoplasm. In view of the restricted volume, organelles are often exposed to large complexes without any interference or intervention. In the specific situation of mitochondria, crashes and entanglements have catastrophic implications, such as cytochrome c secreting into the cytosol and the triggering of apoptosis. Therefore, at this point, it is assumed that cells are endowed with mechanisms that enable mitochondria to release potential tensions arising from collisions with other molecular structures, and hence, it needs to be analyzed how mitochondria deal with intracellular confinement.

6.11.1 Mitochondria Are Affected by the Actin-Driven Movement of Shigella Flexneri The question is how the mitochondria deal with being attacked by an intracellular, rapidly moving specimen (Helle et al. 2017). Shigella flexneri microorganisms are pathogenic bacteria from the family Enterobacteriaceae, and an infection in humans results in diarrhea and heavy inflammation in the intestines. When entering the cytoplasm of diseased cells, a specific subpopulation of bacteria captures the actin cytoskeleton and activates its polymerization on the bacterial surface, creating an actin comet tail (Ray et al. 2009), allowing them to drift rapidly through the cytoplasm and reach velocities of up to 0.5 µm/s (Gouin et al. 1999). U2OS or COS7 cells were virally infected with fluorescent labeled S. flexneri and mitochondria viewed using BFP labeled mitochondrial matrix (mtBFP) (Kanfer et al. 2015). Time-lapse microscopy enabled us to monitor that bacteria frequently

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collided with mitochondria and displaced the mitochondrial tubules to the side, upward or downward. Clashes in some instances led to a noticeable decrease in mitochondrial fluorescence, suggesting that the matrix was narrowed. The mitochondria were cleaved within one to five minutes at the narrowed site in 60% of these occasions. In contrast, only 4% of non-stimulated mitochondria were cleaved within five minutes. Mitochondrial cleavage and fusion are two contradictory phenomena that govern mitochondrial morphology and interconnectivity. Both biochemical processes are strongly regimented and peak in the specific enrollment of dynamin-related GTPases that drive mitochondrial cleavage and fusion (van der Bliek et al. 2013). The cleavage GTPase DRP1 builds up as homogeneous rings around the mitochondria and exploits the energy of GTP hydrolysis to crush and cleave the mitochondria (Francy et al. 2015). To judge whether the collision-associated events of mitochondrial partitioning concerned the canonical splitting apparatus, bacterial motions were mapped cells lacking DRP1. The depletion of DRP1 can be performed by three different methods: firstly, the treatment of cells with siDRP1, secondly, the lentiviral transduction of shDRP1 and thirdly, the CRISPR-driven mutagenesis of the exon 2 with DRP1CRISPR. All possible experimental conditions led to an effective decrease of DRP1 levels and to an increased presence of mitochondria in both mock-infected and Shigella-infected cells. Mitochondria originating from DRP1-depleted cells were severely concerned by bacterial motility. They were pressed and towed and occasionally conspicuously diluted by narrowing. Contrary to wild type cells, mitochondria recovered from DRP1CRISPR cells in 100% of those cells without cleavage, and despite the severe loss of matrix staining during mechanical activation of mitochondria, mitochondria persisted permanently united. In the same way, DRP1 siRNA treatment abolished mobile Shigella-induced cleavage in wild type cells. These results suggest that DRP1 is required for bacterial-driven cleavage. To verify that the cleavage occurrences seen in wild type cells were true cleavage phenomena (Friedman et al. 2011), the targeting of the mitochondrial cleavage apparatus to these fission sites needs to be observed. Consistent with other reports, in uninfected cells, the fluorescent protein-tagged DRP1 has been seen mainly to be diffusely localized in the cytosol with brighter spots in the mitochondria that seem to be stably connected to mitochondria, whereas a subset of this marker fusion-protein highlighted cleavage sites. After the infection with Shigella, the DRP1-driven arrangement of focal points was directed to sites where moving bacteria intersected mitochondrial tubules. In fact, at these specific sites, mitochondria were cleaved. Incidents were also reported in which Shigella encountered mitochondrial areas that were characterized by a low DRP1 level signal that evolved into a more intense puncture on impact and ultimately resulted in cleavage. Together with the DRP1 deletion patterns, these findings point to the fact that mitochondria respond to collisions with bacteria by undergoing active cleavage. The variability in elapsed time between Shigella influence and possible cleavage can represent stochastic variations in DRP1 enrollment and activating kinetics.

6.11.2 The Fission of Mitochondria Can Be Triggered with an Atomic Force Microscope How did the mitochondrial splitting device sense the presence of the bacterium? A possibility is that the verification is biochemical, due to factors located on the bacterial membrane surface. An alternative hypothesis is based on a mechanical verification that the mechanical forces caused by the collision initiated mitochondrial cleavage.

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Mechanical Force Evokes Mitochondrial Fission

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To identify whether mechanical probing is able to directly impose the cleavage of mitochondria, atomic force microscopy (AFM) was utilized (Binnig et al. 1986). More precisely, using AFM, the forces sensing or transmitting can be analyzed in the range of nanonewtons through a cantilever that carries a tip to probe the cells (Müller and Dufrene 2011). It is known that adhesive cells possess a flat periphery (typically 100–200 nm thick), when they are fully spread (Xu et al. 2012); thus, it seems to be possible that a pressure probing of these peripheral regions may cause a deformation of the cell’s membrane surface and thereby transduce forces toward nearby mitochondria. With round cantilever tip, a specific amount of force, such as 15 nN, can be exerted to cells, which are transfected with an outer membrane marker, such as mCherry-Fis1TM that is fusion protein of the mCherry fluorescent molecule to the transmembrane domain of Fis1 and hence identifies the outer mitochondrial membrane and co-stained with Mitotracker Deep Red identifying the mitochondrial matrix. The force probing with the AFM technique causes a constriction of the mitochondria similarly as it has been observed with the decrease of the mitotracker and the mCh-Fis1TM intensity. It is common for mitochondria to be split within one minute of approaching the tip, rarely between 1 and 5 min. To verify that the mitochondria had truly been cleaved, the AFM tip was withdrawn 50 s after the cleavage event and the cells remained imaged for a further approximately 100 s. The cells were then clearly divided by the AFM tip and a constriction ring of the mitochondrial matrix has been seen at the division site. As noticed in the Shigella model, the time lag between peak attachment and mitochondrial cleavage tended to vary, probably mirroring the splitting apparatus recruitment. In fact, these results depended on DRP1, as the repetition of these experiments in DRP1-deficient cells caused a reduction of cleavage processes during force exertion. The mitochondria in DRP1-deficient cells were noticeably constrained at and during 5 min of force exertion, whereas they generally relaxed after retraction of the tip from the force impulse. Although the force exertion can be precisely determined at the AFM tip, however, it is impossible to reveal the force fraction that is exerted to underlying mitochondria, since the cortical actin scaffold can additionally be exerted by a force fraction. The forces required to deform the actin cytoskeleton are in the range of several nanonewtons (Hadjiantoniou et al. 2012).

6.11.3 Mitochondrial Cleavage of Cells Placed on Patterned Substrates External forces exerted by an AFM cantilever tip may be irrelevant for the cleavage of mitochondria. For this reason, an experimental system was needed in which the cells themselves were directly involved in force generation. It can be hypothesized that the morphological plasticity is a physical property of multiple cell types to remodel themselves due to environmental cues. When cells grown inside of dense and complex tissues, they are subject to ongoing deformation that can even impact their mitochondria. These environmental conditions can be rebuilt in vitro by culturing U2OS cells on uneven surfaces with grooves that are approximately 40 µm deep and 80 µm wide and possess flat edge between neighboring grooves, such as vinyl records (Read 1952). Hence, the question can be raised whether cells grown on vinyl records spreading over the edge into the groove can restrict the edge region to peripheral cytoplasmic content without mitochondria. In order to proof this hypothesis, cells have been analyzed that express an ER marker, such as GFP-Sec61b, marking the cytoplasm (Kanfer et al. 2015). In fact, the 3D reconstruction of the spread cell over the edge region revealed that the cytoplasm seems to be confined over the edge region. These constricted sites seem to be regions with elevated mitochondrial cleavage activity. In fact, the cytoplasmic regions of the cells covering the edge of the groove tended to be free of mitochondria. In order to analyze whether these regions display elevated cleavage activity of mitochondria, time-lapse microscopy can be employed,

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and subsequently, a focus can be set on the subset of cells that still possess intact mitochondria. This subset of cells has just begun to cover the edge region and start immediately to cleave their mitochondria. These results support the hypothesis that cells in constricted areas due to their adhesion to a patterned surface can cleave heir mitochondria. The mitochondrial matrix at the edge of the groove showed complete division into about 80% of wild type cells, whereas the cleavage rates in DRP1 siRNA-treated and DRP1CRISPR knockout cells are reduced to only 15 and 43% that exhibited a complete cleavage of the mitochondrial matrix, suggesting that the canonical cleavage system is essential for this process. The milder effect of the CRISPR-mediated DRP1 knockout compared to the siRNA-mediated knockdown can be a result of adjustments to long-term DRP1 depletion. Cell adhesion to uneven surfaces can cause mitochondria to undergo DRP-mediated cleavage in narrowed cytoplasmic areas.

6.12

The Inhibition of the ER or the Dynamics of Actin May Not Significantly Alter the Force-Induced Fission

Without any stimulation, the ER tubules can highlight the sites for mitochondrial fission (Friedman et al. 2011). The ER seems to be entangled around mitochondria and thereby can facilitate the constriction of mitochondria before their fission starts that appears to be regulated by the locally restricted polymerization of actin through the activity of inverted formin 2 (INF2) (Korobova et al. 2013) and Spire1C (Manor et al. 2015). However, in AFM activation of U2OS cells, which stably express both a mitochondrial (mtBFP) and an ER (GFP-Sec61b) marker, it was found that the tipfacilitated indentation of cells displaced the ER from the site of force exertion and subsequent causes the mitochondrial cleavage. However, this seems to be contrary to the hypothesis that the ER fulfills an important role in the force-based cleavage of mitochondria. Hence, it has been investigated the effect of perturbed dynamics of the ER on mitochondria function and thereby employed cells that overexpress the dominant-negative Atlastin (ATL) mutant or the CLIMP-63 mutant. More precisely, ATLs represent large GTPases facilitating the fusion of ER tubules through the assembly of transhomooligomers on the cytoplasmic ER. The overexpression of a GTPase-dead mutant, such as ATL1K80A, or overexpression of the cytoplasmic domain, such as cyto-ATL2, had both a dominantnegative effect on endogenous ATLs and subsequently they impaired the fusion of ER tubules (Goyal and Blackstone 2013; Pawar et al. 2017). In contrast, CLIMP-63 is able to sheets of the ER, and hence, its overexpression fuses ER tubules mainly to ER sheets (Goyal and Blackstone 2013). Obviously, in cells overexpressing ATL1-K80A or Cyto-ATL2, ER assumed a hair-like, hypoconnected morphology, which raised the number of cytoplasmic regions lacking ER. Inversely, the ER was transformed from tubules to plates in CLIMP-63-overexpressing cells. The mitochondrial matrix in these cells displayed typical morphology. In cells located at the edge of vinyl grooves, mitochondria were cleaved at the edge, similar to wild type cells. Some of these cleavages arose in areas with ER sheets only or without ER. The vast majority of cells overexpressing ATL1-K80A and CLIMP-63 exhibited a full cleavage of their mitochondrial matrix. Subsequently, cells expressing cyto-ATL2 were also exposed to Shigella, and a motile Shigella triggered the mitochondrial cleavage even in regions without an ER signal. Although ER could play a role in force-induced cleavage, this function does not seem absolutely essential. In addition, the role of actin in mechanically facilitated mitochondrial cleavage has been studied, since actin polymerization appears to be a key downstream step in ER-tubule-based cleavage through the ER-anchored isoform of inverted formin 2 (INF2). The knockdown of the ER-associated isoform of INF2 had no apparent influence on collision-induced cleavage into Shigella-infected cells or cells cultured on patterned substrates, such as vinyl records. In fact, the collisions found in INF2-deficient

6.12

The Inhibition of the ER or the Dynamics of Actin May …

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cells causing a cleavage process are not pronouncedly altered to cells treated with scrambled siRNA. It is interesting to note that actin-based Shigella motility was not impaired by INF2 depletion, probably due to the usage of Shigella N-WASP and Arp2/3 to encourage actin polymerization (Gouin et al. 1999; Ray et al. 2009). In the same way, 83% of cells cultured on gramophone recordings and exhibiting a constricted cytoplasm had isolated mitochondrial populations, similar to what in control cells. These results suggest that the INF2-facilitated route is not required for the mechanically triggered cleavage. To tackle more generally when actin fibers were essential for force-induced cleavage, cells were exposed to cytochalasin D (cytD), a widely applied drug that abolishes the actin polymerization. Since the actin cytoskeleton is crucial for Shigella motility and for the correct distribution of cells on vinyl plates, the actin cytoskeleton has to be disturbed in AFM experiments. In effective concentration, the drug strongly alters the actin cytoskeleton as evaluated by LifeAct-GFP. Therefore, actin polymerization seems not to be a mandatory step in force-induced cleavage of mitochondria.

6.13

Force-Sensing Mechanism and the Recruitment of DRP1

It was observed that mechanical forces induced the recruitment and activation of the cleavage effector DRP1 and how such a mechanical impulse was perceived at the molecular level. DRP1 is present in the cytosol and is enrolled into the mitochondria by employing integral mitochondrial membrane adaptor molecules such as the mitochondrial cleavage factor (Gandre-Babbe and van der Bliek 2008; van der Bliek et al. 2013) and mitochondrial dynamic protein of 49/51 kDa (Mid49/51) (Palmer et al. 2011; Zhao et al. 2011). These two adaptor proteins seem to able to target DRP1 to the predefined cleavage regions (Friedman et al. 2011). The mitochondrial fission factor can be highly concentrated in dot-like structures within mitochondria, which is regulated in an independent way to the availability of DRP1 (Otera et al. 2010). Hence, it can be hypothesized that adaptor proteins function as mechanosensory molecules located on the membrane surface of mitochondria. To verify this hypothesis, the localization of the mitochondrial fission factor can be determined on the mitochondrial surface using immunofluorescence. This analysis was examined in cells lacking DRP1 in order to rule out that DRP1 did not drive the localization of the mitochondrial fission factor. It has been observed that the mitochondrial fission factor is localized in specific foci independent of DRP1, which is in line with a previous finding (Otera et al. 2010). Moreover, the mitochondrial fission factor seems to be concentrated at constriction regions that are built even on non-perturbed tubules of the mitochondria over time. This behavior has been seen in living cells, which were transfected with GFPtagged mitochondrial fission factor (Friedman et al. 2011). In order to figure out whether mitochondrial fission factor manages to be highly concentrated in force-initiated constrictions, DRP1depleted cells were placed on patterned surfaces, such as vinyl records. In fact, the mitochondrial fission factor has been accurately detected in constricted regions of the mitochondria adhering to the groove edge, suggesting that the mitochondrial fission factor can sense the mechanical properties at the constricted region in the mitochondria through the use of factors located upstream of DRP1. In the same way, pronouncedly reduced cleavage events have been determined in cells lacking the mitochondrial fission factor than in wild type cells, when the either the Shigella setup or vinyl records have been employed. To detect this phenomenon in living cells, lentiviruses expressing GFP-tagged mitochondrial fission factor have been transfected to cells in order to monitor the localization upon mechanical probing with bacteria or patterned topologies. In addition, the expression of DRP1 can be reduced by employing siDRP1. In fact, a pronounced mitochondrial constriction can be seen, when the mtBFP signal is reduced. In addition, the GFP-mitochondrial fission factor was targeted to these constriction

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regions in both mechanical model systems. Hence, the mitochondrial fission factor accumulates referentially to flat mitochondria and may act as a mechanosensor for the cleavage system. In these experiments, it has been seen that there are mitochondrial regions to which the mitochondrial fission factor is mainly targeted, independently of Shigella- or pattern-based mechanical probing and even in the absence of mechanical probing. At these sites, a decreased mitochondrial matrix-targeted mtBFP signal has been detected, which points out to a decreased diameter of the mitochondria. The outer mitochondrial membrane (OMM)-directed fluorescence marker, termed mCh-Fis1TM, revealed that these mitochondria still possess connectivity, since these thin, GFPmitochondrial fission factor-positive, matrix-negative tubules are associated to thick regions of mitochondria. These two heterogeneous regions remained interconnected even for several minutes, while they were still clearly and stably isolated topologically into two distinct mitochondrial zones. However, these two distinct structures can solely be detected when GFP-mitochondrial fission factor is highly overexpressed, which may be just can artifact of the overexpression and no actual cell property. It seemed that the mitochondrial fission factor not only functions as a sensor, but also potentially serves as an initiator of mitochondrial constriction. Since it appeared paradoxical that the mitochondrial cleavage factor operated both as a sensor and as a trigger for mitochondrial constrictions, the question arose whether these two properties might be mutually connected. In order to answer this question, computer-aided Monte Carlo (MC) simulations were used to model a typical protein with specific affinity for narrow regions of the mitochondria. To model the intended affinity, a protein with a curved membrane-binding surface emerged and multiple copies were laid on a membrane tube whose radius exceeded that of the protein’s attachment surface. Once the optimal ratio of the protein and tube diameters needed for protein compounding had been determined, a first simulation was carried out in which the protein density was arbitrarily low. Under these specific conditions, the protein can diffuse freely on the mitochondrial membrane tube, and hence, it is still homogenously distributed. The simulation was repeated; however, this time a narrowing in the membrane tube to imitate mechanical stimuli had been chosen and it has been seen that the proteins are targeted to the narrowing region, which clearly indicates that the mitochondrial fission factor was recruited to mechanically probed regions. The next step was to repeat the simulation using a high protein density. Under these conditions, the proteins spontaneously tightened the membrane tube without prior narrowing. The simulation outcomes closely rebuilt the mitochondrial cleavage factor-stabilized, matrix-free thin mitochondrial regions, which were characterized by a remarkably high level of overexpression of the mitochondrial cleavage factor. Consequently, these findings imply that mitochondria react not only to biochemical but also to mechanical signals. The mechanical force, causing the deformation of mitochondrial membranes, facilitated the cleavage of mitochondria that connects a mechanical event with a biochemical reaction. How is this reaction organized on the molecular scale? A previously unknown property of mitochondrial fission factor is reported to have a preference for smaller diameter mitochondrial tubules. Thus, a simple model for force-based cleavage is that the physical narrowing could decrease the mitochondrial diameter that leads to the concentration of the mitochondrial fission factor and consequently to the assembly of DRP1 and the cleavage of mitochondria. The mitochondrial fission factor can not only have a mechanosensory effect, but can also additionally trigger the narrowing in the mitochondria. The Monte Carlo simulations demonstrate that a protein with an increased affinity for mitochondria with a narrower diameter can be quickly aggregated at narrow locations. At high concentrations, on the contrary, it will initiate and stabilize narrowing, two behaviors that imitate what is experienced with the GFP mitochondrial cleavage factor in living cells. Therefore, the tendency of a protein to pinpoint narrow sites is inextricably linked to its ability to stabilize narrowed regions. The distinct behaviors, which differ in the initiating and sensing reactions, seem to be regulated by the amount of available protein. In fact, the

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mitochondrial fission factor performs a stabilization functions of narrowed region in mitochondria, when it is overexpressed, which is similarly to the function of other membrane curvature-sensing proteins (Baumgart et al. 2011; Simunovic et al. 2015; Sorre et al. 2012). It is noteworthy that the protein simulated is not intended to reproduce the form of the mitochondrial fission factor. However, the simulations show that capturing and inducing narrowrings are two sides of the same medal and that the difference between capturing and induction is in the degree of expression of the factor. As a matter of fact, it is unclear whether mitochondrial fission factor monomers or oligomers possess a curved membrane-binding interfacial region, such as the model protein in the simulations. The affinity of the mitochondrial fission factor for narrowed mitochondria may arise from other factors such as the affinity for specific lipid species or the properties of its transmembrane domain. There are several other DRP1 adapters in the cell that point to a large number of factors that influence DRP1 enrollment. The mitochondrial cleavage factor is probably not the only force sensor present on the mitochondrial surface. Similarly, it is improbable that force is the only cause of mitochondrial cleavage. In undisturbed adhesive cells, mitochondrial cleavage occurs almost exclusively at sites that come into direct physical contact with the ER (Friedman et al. 2011). The force-induced mitochondrial cleavage is a mechanistic justification for this phenomenon. In fact, adherent cells have a very flat circumference (Xu et al. 2012), with only a single ER tubuli layer, which renders them particularly suitable for microscopic investigations. Therefore, the ER-mediated mitochondrial division is mainly examined in peripheral areas of adherent cells. Mitochondrial tubules tend to be wider in diameter of about 500–700 nm (Youle and Karbowski 2005) than the cytoplasm thickness in these peripheral areas. In fact, high-resolution imaging techniques demonstrate that peripheral mitochondria are flattened by the proximity of the “ventral” and “dorsal” actin cortical fibers (Huang et al. 2008, 2016), assume an elliptical—and not a tubular—cross section, and slightly curve away from the cell surface (Wojcik et al. 2015). In high-speed AFM measurements of living cells, it can be detected that mitochondria distort the plasma membrane from the nearby actin cytoskeleton of about 50 nm (Yoshida et al. 2015). It is obvious that peripheral mitochondria trapped between the dorsal and ventral actin cortices are mechanically restricted in these regions. Why would the pressure not divide the peripheral mitochondria? Presumably, the pressure of the actin cortices is uniform over the entire length of the mitochondria and does not cause local contractions in which the mitochondrial fission factor and DRP1 accumulate. In this connection, it is appealing to suggest that crossing-over ER tubules, solidified by an INF2- and Spire1C-mediated actin envelope, could exert localized mechanical forces on the underlying mitochondria, initiating cleavage at the ER–mitochondria contact points. As it is the most extensive intracellular membrane system, the ER network physically collides with the mitochondrial network, which accounts for the frequent occurrence of cleavage at ER contact sites. In furtherance of this concept, the mechanically stimulated cleavage process no longer looks to be dependent on ER tubules. An encouraging hypothesis is that ER is an intrinsic reservoir of mechanical force and therefore seems to be dispensable under the conditions of this experiment because an external mechanical force is available. In turn, this implies that the ER exerts a force rather than providing biochemical signals in ER-mediated cleavage events. Since actin is an essential force generator in the cell, the model could also help elucidate why it is crucial for mitochondrial cleavage (Korobova et al. 2013; Manor et al. 2015), except when cells are mechanically exited by an AFM. The role of mitochondrial fusion and cleavage has been left unclear. Both processes are suggested to serve in the repair of damaged mitochondria by merging with healthy mitochondria, in the severance of terminal injured mitochondria for waste disposal by mitophagy and in the proper distribution of mitochondria to daughter cells in mitosis (Katajisto et al. 2015; Twig et al. 2008). Mitochondria are cleaved by DRP1 and mitochondrial fission factor-mediated cleavage when mechanically triggered. Cells continuously pass through incidents such as organelle transport,

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expansion and contraction of the cytoskeleton and the overall reconstruction of the cytoplasm. Organelles which are as extensive as the ER, and the mitochondria must deal with a constantly evolving natural environment and prevent conflicts and enmeshment. By eliminating all the consequential mechanical stresses, cleavage could allow mitochondria to avoid shearing and rupturing, leading to cytochrome c secretion and apoptotic cell death and preventing entanglements. In this property, DRP1 activity can occur in parallel with the activity of topoisomerase-II during genome unbundling. By using serial block surface scanning electron microscopy, interlinkages between mitochondria and ER in mammalian cells are detected. Due to the dynamics of these organelles, it is likely that entanglements lead to a mechanical loading of the mitochondria and thus to their dissolution by cleavage. In contrast to mitochondrial cleavage, ER cleavage is not frequently encountered. Therefore, it is assumed that mitochondrial cleavage is the principal mechanism for loosening these linkages. Mechanically initiated mitochondrial alterations might be of particular relevance for cell migration through narrow passages such as leukocyte extravasation and immune monitoring (Muller 2011; Nourshargh and Alon 2014) as well as for the malignant progression of cancer (Friedl and Wolf 2003). In conclusion, it has been demonstrated that the mechanical force can be connected to a distinct cellular process that is critical for the shaping and the localization of the intracellular membrane and therefore seem to offer a new view on the dynamics of mitochondria and the interaction of mitochondria with other organelles.

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7

Mechanical View on the Endoplasmatic Reticulum and Golgi

Abstract

This chapter characterizes the most important cellular organelles such as the endoplasmatic reticulum and the biological and physical functions based on the Golgi apparatus and relates the properties of these organelles tothe overall mechanical properties of the cell. More specific, the connections between the endoplasmatic reticulum and the Golgi apparatus are presented. All cell organelles consist of highly specialized subcompartments fulfilling distinct tasks, and hence, they provide on this discrete level a higher organization of the overall cell. What we understand about the function of organelles is mainly based on microscopic observations and less on the effects of stress on the structure and function of organelles, which is also addressed and discussed.

7.1

Introduction to the Endoplasmic Reticulum (ER)

A characteristic feature of the endoplasmic reticulum (ER) is the enormous surface area of the organelle. In detail, the ER can be described as an organelle consisting of a series of concentric spheres located around the nucleus of the cells. The ER was originally classified as a lamellar system of relatively flat and thin cavities with uniform and parallel localization (Fawcett 1966) (Fig. 7.1). In addition, the ER represents a large structure that is dynamically remodeled. The ER fulfills several roles in the cell such as the storage of intracellular calcium, protein synthesis and the metabolism of lipids. Different functions of the ER are spatially separated and hence performed in distinct domains of the ER, such as ER tubules, ER sheets and ER-drived nuclear envelope. However, the overall architecture and dynamics of the ER are based on different proteins, and how the ER changes in shape due to cellular signals, cell type, cell cycle phase and during the development of the organism, remains open (Schwarz and Blower 2016). Hence, the current knowledge of the ER dynamics is presented and discussed. Finally, it is described the responses of the ER are coordinated in the differed layered structures of the organelle. The ER constitutes the largest organelle of the cell and plays a key role in various cellular processes. Among them are firstly the protein synthesis, folding and transport, secondly, the lipid, steroid and carbohydrate metabolism and thirdly the deposition of calcium (Reid and Nicchitta 2015; Rapoport 2007; Braakman and Hebert 2013; Fagone and Jackowski 2009; Hebert et al. 2005; Clapham 2007; Westrate et al. 2015). Since the ER fulfills multiple different functions, numerous specific proteins are necessary for these different tasks and unique physical structures enable the © Springer Nature Switzerland AG 2020 C. T. Mierke, Cellular Mechanics and Biophysics, Biological and Medical Physics, Biomedical Engineering, https://doi.org/10.1007/978-3-030-58532-7_7

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Fig. 7.1 The structure of the ER. The ER can be separated into three major domains, the sheet-like ER, the tubular ER and the nuclear envelope. The sheet-like ER contains numerous ribosomes and is termed the rough ER. The tubular ER possesses only a few ribosomes and is termed the smooth ER. The sheet-like and tubular ERs are highly dynamically remodeled and can switch between the two configurational states. The ER has the capacity to fuse, elongate and branch

coordination of the ER with the intracellular microenvironment and facilitate the response to varying intracellular cues. The ER consists of several different structural areas, each of which is assigned to one or more functions. However, how these subdomains function and how they are organized is still elusive. Hence, it needs to be identified how the function of an individual domain determines its structural arrangement. Are the different functions of the subdmains the determining factors for the structural arrangements of certain subdomains?

7.2

The Structure of the ER

There general structure of the ER structure has been reviewed in great detail (Westrate et al. 2015; Engish and Voeltz 2013; Friedman and Voeltz 2011; English et al. 2009; Shibata et al. 2006; Hu et al. 2011). The structure of the ER has been coupled to alterations of the ER shape due to cellular or environmental signals (Schwarz and Blower 2016). The basic structure of the ER is characterized by two distinct elements, the nuclear envelope and the peripheral ER. More precisely, the peripheral ER consists of smooth tubules and rough sheets. The ER represents an interconnected network that is enclosed by a continuous membrane. Different structures of the ER enable it to fulfill various special tasks within the cell. In specific detail, the envelope of the nucleus is composed of two lipid bilayers: the inner nuclear membrane (INM) and outer nuclear membrane (ONM). The nuclear envelope exhibited a common lumen with the surrounding peripheral ER. The several hundred pores located within the ONM and spanning to the underlying INM of the nuclear envelope to provide molecular transport that encompasses proteins and RNAs with different degrees of diffusion or controlled transport according to molecule size. The nuclear envelope is linked to sheets or cisterns that comprise part of the peripheral ER. The sheets are thin and made of two lipid bilayers with a lumen between them, with the curved areas positioned exclusively at the membrane edges. The size of peripheral ER leaves is subject to fluctuation, although the luminal spacing is very uniform, typically approximately 50 nm for mammals and 30 nm for yeasts (Bernales et al. 2006) (Fig. 7.2). Sheets are generally surveyed in

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Fig. 7.2 ER sheets are characterized by CLIMP63 luminal bridges and contain several ribosomes on the membrane. ER tubules at three-way junctions are stabilized through Atlastin/Sey1p bridges. Both ER structures display Reticulin/DP1/Yop1p oligomeric arcs

a layered conformation and linked to helical edges via areas of twisted membranes (Terasaki et al. 2013). The rough sheets of the ER are containing a high density of ribosomes on their cytosolic surface (Shibata et al. 2010; West et al. 2011). Moreover, these rough sheets are the major sites of the synthesis of proteins, their precise folding and the post-transcriptional modification that can traged proteins to the membrane, nucleus, ER, Golgi apparatus or even for secretion by the cells. However, less ribosomes are located on the outer membrane surface of ER tubules (West et al. 2011) that exhibits a curved and smooth surface that cannot support the interaction with large polysomal structures. In fact, the tubular scaffold is highly dynamic, since it undergoes continuously rearranging and even rebuilding, and is more specifically arranged in three-way junctions linking the individual tubules (Fig. 7.3). Although tubules and sheets exhibit widely divergent structural characteristics and therefore participate in a variety of cell processes, the luminal distance between tubules and sheets is similar (Bernales et al. 2006; West et al. 2011). Remarkably, ER tubules and sheets are present in all eukaryotic cells (Staehelin 1997), although the ratio of sheets to tubules differs in various cell types and mirrors the specific features of these cells. The ER framework of dedicated cells that produce high amounts of secreted proteins, such as pancreatic secretory cells and B cells, is largely made up of sheets. Cells engaged in functions such as lipid synthesis, calcium signaling and points of contact for other organelles inherit an ER consisting mainly of tubules. Adrenal, liver and muscle cells exemplify specialized cells with a generally tubular structure and accurately mirror the functionality of these cells (Baumann and Walz 2001). Another peripherally located ER arrangement is the cortical ER, which is attached to the plasma membrane and has an intermediate phenotype between sheets and tubules with both curved and flat membranes. Calcium signaling takes place at the interface between the plasma membrane and the adjacent cortical ER and is required for contraction of the muscle (Block et al. 1988; Takeshima et al. 2000). For this reason, the structural morphology and intracellular distribution of ER subdomains determine the function of these subdomains and consequently the function of the differentiated cell within they are situated. Advanced microscopic techniques have made it possible to distinguish between various ER structures and the proportions of these features in highly specialized cell types. When the function of these cells in the organism is examined, the type and quantity of peripheral ER present clearly mirrors their function. It is not yet understood how these proportions are achieved and in what manner cellular signaling pathways interfere with the characterization of the ER type which will be dominant in a specific cell type.

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Fig. 7.3 Three-way functions are presented in detail, which are formed through ER tubules

7.3

The Shaping of ER Proteins

7.3.1 ER Tubules Peripheral ER structures are as different and multifaceted as the amount of proteins that provide their conformation. In precise detail, various proteins have been designated that favor specific ER structures, although probably the brightest group of proteins investigated comprises the reticulone family of proteins targeting tubules and the strongly curved edges of ER sheets (Shibata et al. 2010; Voeltz et al. 2006). By producing a transmembrane hairpin topology serving as a spike, these integral membrane proteins promote membrane bending and push lipids into the outer wings of the bilayers, resulting in membrane bending (Voeltz et al. 2006). These proteins usually arrange to oligomers and are pronouncedly less mobile than other proteins located in the ER (Shibata et al. 2008). The overexpression of some reticulone isoforms results in the development of long ER tubules at the cost of the sheets (Shibata et al. 2008). The degradation of reticulones and thus the capacity to bend membranes subsequently results in a decrease in the amount of ER tubules, which entails an expansion of the peripheral sheets (Voeltz et al. 2006; De Craene et al. 2006; Anderson and Hetzler 2008). For this reason, the content of reticulones inside a cell defines the occurrence and microstructure of ER tubules. Reticulones are not only effective in the forming of ER tubules. Members of the DP1/Yop1/REEP5/6 and REEP1-4 families, who are ample ER-resident proteins targeting specific tubules and edges of sheets, also serve as stimulating factors for tubules. The two proteins DP1/Yop1 and REEP5/6 (Hu et al. 2009) possess a very similar transmembrane hairpin structure in common with the reticulons that causes the stabilization of the bended membranes of tubules (Voeltz et al. 2006; Shibata et al. 2008; Hu et al. 2008). Specifically, the topology of REEP1-4 proteins is somewhat altered from that of REEP5/6, implying that these proteins may perform slightly differing functions in the structuring of ER from closely associated REEP5/6 proteins (Park et al. 2010). Beyond that, purified reticulones and proteins of the DP1/Yop1 family succeeded in inducing the generation of tubules out of the purified vesicles (Hu et al. 2008), revealing that these proteins have a substantial role in the growth of ER tubules.

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Reticulons and DP1/Yop1 encourage tubule development; however, other factors are necessary to foster the establishment of the tubular mesh and typical three-way connections by homotypic fusion. Atlastins belong to the dynamin-like family of GTPases that facilitate the homotypic fusion processes. Depletion by RNAi or the expression of dominant-negative atlastine within cells results in a deficiency of fusion occurrences, causing a plethora of elongated, straight tubules (Hu et al. 2009). When a dominant-negative cytoplasmic fragment of Xenopus comprising the GTPase domain and lacking the transmembrane domain and cytoplasmic tail (Bian et al. 2011) is transferred into Xenopus interphase extracts, the establishment of the ER framework was inhibited (Wang et al. 2013). Similar point mutations, impeding dimerization of the cytoplasmic partition of human atlastin (Byrnes and Sondermann 2011), were carried out in the cytoplasmic part of the atlastin protein of Xenopus, injected into interphase extract and had no influence on the generation of the ER matrix (Wang et al. 2013). In addition, antibodies directed against atlastin prevent the development of ER systems when inserted into extracts of Xenopus oocytes (Hu et al. 2009). In Drosophila, exhaustion of atlastine fragments the ER and purified atlastine is capable of triggering GTP-dependent fusion of proteoliposomes (Bian et al. 2011; Byrnes and Sondermann 2011; Orso et al. 2009). Hence, investigations of a high number of organisms, extracts and purified proteins revealed that atlastin seem to be crucial for the induction of the homotypic fusion of vesicles between the ER membranes that is required for the general function of the entire meshwork. Several new major constituents of the ER dynamics have been revealed. It has been shown that purified ER vesicles of Xenopus eggs need GTP for the homotypic fusion of ER vesicles, when there are no cytosolic factors available (Voeltz et al. 2006; Dreier and Rapoport 2000). These results are in line with other reports stating that GTPases are necessary for the process of ER fusion (Turner et al. 1997; Audhya et al. 2007), and even more recently, it has been employed a proteomics approach that determined Rab10 as a parameter essential for the assembly of the ER (English and Voeltz 2013). Moreover, a knockdown of Rab10 or the overexpression of a dominant-negative point mutant locking GDP in cultured human cells leads to elevated levels of RAb10 in ER sheets, whereas it is reduced within ER tubules (English and Voeltz 2013). The fusion between ER–ER regions occurs at locations with increased Rab10 levels. Rab10 has been seen to be co-localized with specific lipid-synthesizing enzymes such as phosphoinositol synthase (IS) and choline/ethanolamine phosphotransferase (CEPT1) (English and Voeltz 2013), which can be not yet identified subdomain or subcompartment of the ER. However, it is still not well understood what function the Rab10 fulfills during the fusion of the ER vesicle or even how the homotypic fusion of ER vesicles is connected to the synthesis of lipids. It has been demonstrated that Rab18 performs a functional role in the process of ER dynamics, since it is positioned in the ER nearby the Rab3 GTPase-activating protein (GAP) complex. When Rab18 is eliminated, the phenotype is close to that of the phenotype seen after Rab10 inhibition (Gerondopoulos et al. 2014). When Rab10 is deficient, Rab18 is able to redisperse into peripheral sheets (Gerondopoulos et al. 2014). Thus, it also seems that the depletion of Rab10 or Rab18 impedes the stability of ER tubule fusion and decreases the density of tubules, with the result of an increase in ER sheets. The depletion of Caenorhabditis elegans RAB-5, previously involved in early endosome function (Stenmark 2009), reproduces the peripheral ER imperfections appearing in RET-1 and YOP1 (homologs of Rtn4a and DP1) (Audhya et al. 2007). Besides the role played by RAB-5 in the assembly of the peripheral ER, the kinetics of disassembly of the nuclear envelope in these mutants is concerned (Audhya et al. 2007). More precisely, beyond the role of GTPases, which fulfill a direct function in the homotypic vesicular membrane fusion, there seems to be a key role of lipid synthesizing enzymes in the regulation of the ER shape and its overall organization. The impairment of the C-terminal domain (CTD) of the nuclear envelope phosphatase-1 (CNEP-1), which is highly concentrated in the vicinity of nuclear envelope and thereby induces the membrane phospholipid

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synthesis, elicits the formation of ectopic leaves covering the nuclear envelope and distorting the degradation of the nuclear envelope (Bahmanyar et al. 2014). Consequently, these findings show that the interlinked scaffold of proteins and their functions contribute to the shape of the ER structural architecture. It is widely known that the ER represents a highly dynamic meshwork, which continuously undergoes the remodeling and reordering (Lee and Chen 1998). The tubules of the ER fuse and branch constantly, leading to the formation of new three-way connections. In a concurrent effort, the sliding interconnects and tubule ring closure mechanisms lead to the disappearance of three-way connections and the inherent polygonal structure (Friedman et al. 2010). Although there is less understood how these complexes regulate the precise function of this process, the Lunapark (Lnp1) protein has been found to be positioned in these complexes and thereby can provide a stabilization of the three-way junctions (Chen et al. 2012, 2015). Lnp1 interacts with reticulons and Yop1, and its close positioning to junctions is controlled by Sey1p, which is the homolog of atlastin in yeast (Chen et al. 2012). The lack of Lnp1 evokes a collapsed and tightly reticulated ER network in yeast and human cells in in vitro cultures (Chen et al. 2012, 2015), although even though only half of the crossings are linked to Lnp1 (Chen et al. 2015), mirroring the fluidity of the ER mesh. When Lnp1 is overexpressed, it becomes targeted to the peripheral ER, where is causes the assembly of a complex polygonal tubular scaffold (Moriya et al. 2013). Moreover, the assembly a network structure is impaired by mutations of Lnp1 inhibiting the N-myristoylation (Moriya et al. 2013) that is an attachment of the myristic acid (consisting of a 14-carbon saturated fatty acid), which reveals that this alteration seems to be crucial for the Lnp1-driven processes on the ER morphology. In terms of the membrane translocation, formation of topology and the targeting of the protein to the ER, the Nmyristoylation is not necessary; however, it can fulfill still an important function in providing protein–protein or protein–lipid interactions needed for morphological alterations of the ER, although the precise molecular mechanism of this functions is still elusive (Moriya et al. 2013). The intrinsic mechanism for the Lnp1-facilitated stabilization of three-way compounds is not understood, although recent research and knowledge of the structure and domains within the protein have revealed how Lnp1 fixes the compounds (Chen et al. 2012, 2015). Firstly, Lnp1 possesses two transmembrane domains and a zinc-finger domain that is positioned at the cytoplasmic site of the ER membrane (Chen et al. 2015). However, the polygons are smaller in cysteine mutations within the zinc-finger domain and the regions without cortical ER appear more visible due to an elevated number of mutated cysteines (Chen et al. 2012). Mutations in the zinc-finger domain can hence impact interactions between individual proteins, affect complex generation or disrupt the dispersion of intracellular lipids on the cytoplasmic side of the membrane and have harmful effects on the stabilization of the junctional linkage. Beyond that, the transmembrane domains function as an upsidedown cotter which enhances the local negative bending feature of three-way connections (Chen et al. 2015) and counteracts the positive bending feature of reticulons. It is also conceivable that several Lnp1 proteins can also work together collaboratively to fix the connection, or that Lnp1 intervenes temporarily to fix or alter lipids or other proteins at the connections (Chen et al. 2015). More and more evidence emerged that, in contrast to proteins controlling membrane morphology and dynamics, a modification of the nucleic acid level of ER can also affect the ER form. Initial experiments revealed that a short incubation of cultured tissue cells with the translation inhibitor puromycin, dissociating mRNA-ribosome complexes, results in the removal of ribosomes from ER and the destruction of ER sheets (Shibata et al. 2010; Puhka et al. 2007). Evidence points to the fact that the availability of mRNA-ribosome complexes can help to stabilize ER sheets. To corroborate this hypothesis, an ER-localized ribonuclease, XendoU (Laneve et al. 2003) has been characterized which alters the RNA content of ER in reaction to alterations in the Ca2+ concentration (Schwarz and Blower 2014; Seidel and Peck 1994). These alterations take place at physiological concentrations of

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approximately 1.5 µM, which resembles the release of Ca2+ ions from intracellular and extracellular storage sites at the time of fertilization (Busa and Nuccitelli 1985; Lorca et al. 1991). A subpopulation of XendoU can target the ER, and moreover, it can be co-immunoprecipitated with a large number of proteins that are located in the ER (Schwarz and Blower 2014). The exhaustion of XendoU causes the creation of long, unbranched tubules in Xenopus leavis egg cell extracts, and the recovery of this phenotype demands an intact catalytic activity of the protein, suggesting that proper nuclease activity is crucial for the correct development of an ER framework (Schwarz and Blower 2014). In parallel, the addition of antibodies to purified vesicles induces a barrier in mesh generation, which shows that XendoU affects the surface of ER membranes to adjust the ER architecture (Schwarz and Blower 2014). Curiously, the addition of 5′5′-dibromo BAPTA, which is a powerful calcium chelator, inhibited the fusion of vesicles in this complex (Dreier and Rapoport 2000). The depletion of XendoU results also in a retardation of the replication and sealing of the nuclear envelope (Schwarz and Blower 2014), and BAPTA prevents the production of the nuclear envelope in experiments on the regeneration of Xenopus egg extracts (Sullivan et al. 1993). Collectively, these results point to XendoU working on membranes to decompose RNAs. Vesicle fusion revealed that RNAs were cleaved and liberated from the membrane surface, signaling XendoU’s ability to break down these RNAs and liberate proteins to purify membrane areas that permit a vesicle generation that triggers cross-linking (Schwarz and Blower 2014). Specifically, where purified vesicles with rising concentrations of RNaseA are exposed to the same experiment and handled, an ever more abnormal system of major vesicles that could not merge is created (Schwarz and Blower 2014). Findings from in vitro trials demonstrated that XendoU at membranes becomes activated in conjunction with calcium secretion to decompose local RNAs and vacant membrane sites conducive to controlled fusion to optimize mesh building. Finally, the decay of the human homolog EndoU in cultured human cells, in a similar way to the decay of other proteins involved in tubule generation, causes an enlargement of the sheets (Schwarz and Blower 2014). In addition, the recovery of the phenotype of the expanded sheet was reliant on the integrity of the catalytic activity monitored with recombinant protein in the extract environment. Thus, XendoU exemplifies a protein being activated to respond to cellular signals in order to control proper ER generation, and additional trials may uncover other proteins which are controlled in this fashion to optimize organelle architecture.

7.3.2 ER Sheets Since the assembly and preservation of tubules are discussed, the role of sheets and other peripheral ER structures need to be pointed out. At the first glance, it needs to be figured out how the sheets are generated. In fact, various mechanisms have been proposed, among them the concept of integral membrane proteins crossing the intraluminal space and building bridges linking the lipid bilayers (Shibata et al. 2009, 2010; Senda and Yoshinaga-Hirabayashi 1998). These proteins can fulfill at least two functions, such as the stabilization of the structural architecture of the lipid bilayers or the precisely set distance of the two lipid bilayers. Moreover, all these proteins or complexes are able to build a backbone supporting sheet stabilization or moving the two lipid membranes closer together (Shibata et al. 2009). Several proteins, such as Climp63, p180 and kinectin have been revealed to fulfill their tasks in the generation of ER sheets, their stabilization and maintenance (Shibata et al. 2010). Besides considerably elevated membrane proteins and key factors of the translocon, Climp63, which is a coiled coiled protein with a single transmembrane domain, was investigated together with kinectin and p180 in a mass spectrometric assay for the presence of rich integral ER membraneproteins (Shibata et al. 2010). Employing many different approaches and using vastly varying cell

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types Climp63 can be confirmed to be a largely abundant protein (Foster et al. 2006; Nagaraj et al. 2011; Beck et al. 2011), which is positioned in the perinuclear ER and cannot be found in the nuclear envelope (Klopfenstein et al. 1998, 2001). Extremely stable oligomers of Climp63 are able to accumulate, which limits the mobility of the protein across the membrane and facilitates its localization to the rough ER (Klopfenstein et al. 2001). Climp63 overexpression causes a huge proliferation of ER sheets, whereas the decrease in expression unexpectedly does not produce a reduction in sheets, but rather a narrowing of the gap between sheets (Shibata et al. 2010). In contrast, these sheets are diffusely scattered in the cytoplasm, recalling the phenotype of cells exposed to the translation inhibitor puromycin (Shibata et al. 2010). However, the key elements of the translocon, the protein channel interfering with ribosomes and leading to the translocation of nascent peptides into the ER or the attachment of transmembrane slices of newly synthesized proteins, have been accumulated on sheets (Nikonov et al. 2007). Accordingly, these results indicate that the role of Climp63 in forming sheets appears plausible that supplemental determinants are associated that serve as an overall sophisticated regulatory framework that governs the balance of sheet and tubule growth.

7.4

The Synthesis and Folding of Proteins in the ER

A key function of the ER is that it can be a major site for the production of proteins that can be either secreted proteins or integral membrane proteins (Walter and Blobel 1981), and additionally a specific subset of cytosolic proteins (Reid and Nicchitta 2015). Protein synthesis necessitates the targeting of ribosomes to the cytosolic surface of the ER, and the canonical pathway governing protein synthesis entails the co-translational binding of the mRNA-ribosome composite to the membrane of the ER. The translation of secretory or integral membrane proteins is induced in the cytosol, in which case ribosomes comprising these mRNAs are enrolled into the ER membrane via a signal sequence within the amino terminus of the resulting polypeptide detected and linked by the signal recognition particle (SRP) (Walter et al. 1981; Noriega et al. 2014). The complex of the mRNA-ribosome–nascent polypeptide SRP is aimed at the ER where it attaches to the SRP receptor (Gilmore et al. 1982; Meyer et al. 1982). The translation proceeds on the ER and the developing polypeptide is able to penetrate the ER co-translationally over the translocon (Rapoport 2007), a channel comprising several Sec proteins stretching across the lipid bilayer (Blobel and Dobberstein 1975). During the translation time, or in exceptional cases upon the completed translation (Braakman and Hebert 2013), a signal peptidase can cut off the short signal peptide that then enables the free protein to access lumen of the ER (Siekevitz and Palade 1960). When the newly translated protein is targeted to become an integral membrane protein, which is characterized by a stretch of hydrophobic residues or stop-transfer membrane linkage sequence, the translocation will be held on standby (Potter et al. 2001). Then the protein is transferred laterally and become connected to the phospholipid bilayer where it stays (Potter et al. 2001). There is one type of transmembrane proteins compromising a single hydrophobic stretch domain, which are termed classical single-pass transmembrane proteins. Another type of transmembrane proteins consists of proteins possessing multiple regions that transverse the membrane, which are referred to as multispanning transmembrane proteins (Braakman and Hebert 2013). If the freshly polymerized protein is not a membrane protein and has to be integrated into the secretory pathway or even into the lumen of membrane-bound organelles, the protein is transported directly. After the complete translation process, the signal peptide can be cut off the ribosomes and secreted into the cytoplasm (Seiser and Nicchitta 2000; Potter and Nicchitta 2000). In the case of mRNAs that are translated through stably ER connected ribosomes, the mRNAs are freely secreted into the cytoplasm, while the ribosomes still maintained in an ER connected state and hence can

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perform even multiple rounds of translation processes (Reid et al. 2014; Palade 1955). For cytoplasmic proteins that are translated through the ER connected ribosomes, it is less understood how the targeting of these mRNAs toward the ER is regulated or what type of specific ribosomes are employed to trigger the translational process. However, it has been reported that the ER inherent protein p180 fulfills a crucial role during the targeting process of mRNAs to the ER, which seems to be separated from the translational process (Hicks et al. 1969). After the protein synthesis and the translocation of the entire protein into the lumen of the ER, the protein targeted to secretion need to be folded precisely and post-translational modified through the utilization of chaperones or enzymes facilitating the protein folding. Among these modifications are the N-linked glycosylation, the assembly of disulfide bonds and the oligomerization of the proteins (Fig. 7.4) (Braakman and Hebert 2013). At this stage, these proteins are targeted for their secretion. When the protein performs a function within the ER, such as a chaperone, the correct folding begins. When the protein is intended for secretion, it is freed from the chaperones and boxed for passage through the Golgi to a final location, such as the plasma membrane or secreted to the microenvironment, or is delivered to peroxisomes (Mueckler and Pitot 1981). In addition, the cytosolic parts of the transmembrane proteins can interfere with cytosolic proteins or chaperones to correctly bend these regions.

Fig. 7.4 N- and O-glycosylation of proteins in the ER and Golgi

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However, although there exists a number of proteins and complexes that aid the precise folding of proteins, still a certain fraction of proteins is not in its native and functional state, as this fraction contains proteins, which are misfolded or even clustered in aggregates (Mechler and Vassalli 1975a). There are at least two options, firstly these proteins still continue to be located within the ER or secondly, they manage to undergo degradation via the ER-associated degradation (ERAD) process, which is facilitated by the proteasome that impairs the entry of aberrant polypeptides into the secretory system (Mechler and Vassalli 1975b). The identification of wrongly folded proteins is associated with their degradation through the ERAD pathway, which requires to be strongly regulated in order not to interfere with the functions of the cell (Mechler and Vassalli 1975b). Intriguingly, there exist various pathways for the initiation of an ER stress reaction and subsequently for the creation of pathological conditions for humans. The activation of ER stress reaction signal transduction pathways supports various neurodegenerative diseases that are based on the misfolding of specific proteins, including as Alzheimer’s disease. There is also evidence of activation of the ER stress reaction pathway in diabetes, inflammatory bowel disease and several types of cancer. The role of ER stress reaction pathways in these disorders is an ongoing field of research and several components of stress reaction pathways appear as possible therapeutic objectives (Kopczynski et al. 1998). Under physiological conditions, the protein synthesis in the ER is performed within the restricted ER sheets and it is frequently observed that the structure of the ER is based on the positioning of the RNA and on the stress of the ER; however, it will be discussed after the biogenesis of the lipids.

7.4.1 The Biogenesis of Lipids Since the ER has been revealed as a major site of the synthesis of proteins, it is employed as a site of bulk membrane lipid biogenesis (Fagone and Jackowski 2009) that is located within the endomembrane compartment connecting the ER and the Golgi apparatus. The major constituents of the membranes are proteins and phospholipids and they get transferred and biochemically altered in the region of the ER, which in close neighborhood to the Golgi apparatus (Diehn et al. 2000). This specific region is termed the ER–Golgi intermediate compartment (ERGIC), which contains multiple tubules and vesicles (Fagone and Jackowski 2009) (Fig. 7.5). When the lipids are directed at the ERGIC, they are dispersed in the cell either via organelle contacts or secretory vesicles (Diehn et al. 2006). The cis-Golgi is nearest to the ERGIC compartment and enters the trans-Golgi system, which contains vesicles with freshly produced secretory proteins from the ER form and the bud (Fagone and Jackowski 2009). The trans-Golgi system has historically been regarded as the primary sorting location in the cell, attracting cytosolic freight adapters to link, transport indirectly or directly, proteins or lipids (Lerner et al. 2003).

7.4.2 The Metabolism of Calcium (Ca2+) Since ER has been discovered as an eminent place for the synthesis and transport of a large number of biomolecules, the ER system is also a key storage site for intracellular Ca2+ (Reid and Nicchitta 2012; Stephens et al. 2008). The general physical concentration of Ca2+ ions in the cytosol is around 100 nM, whereas the Ca2+ ion concentration in the ER lumen of the ER is in between 100 and 800 nM, and even in the extracellular microenvironment, the Ca2+ concentration is much higher, such as approximately 2 mM (Clapham 2007; Jagannathan et al. 2011). The ER possesses multiple calcium channels, ryanodine receptors and inositol 1,4,5-trisphosphate receptors (IP3R), which provide the freeing of Ca2+ ions from the ER in the cytoplasm at relatively low levels of intracellular Ca2+

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Fig. 7.5 Overview of the intracellular transport routes. Schematic view of the secretory route and presentation of major coat proteins that facilitate the protein sorting within various cellular compartments. Secretory cargoes are trafficked in an anterograde direction from the ER to the Golgi in COPII-coated vesicles. Sec24 represents the cargo adapter with various cargo-binding sites, such as A, B, C and D, to foster a various set of cargo proteins. The COPI coat drives retrograde transport from the Golgi to the ER and between different Golgi compartments. The cargo-binding subunits of COPI vesicles build an arch-like structure contacting the membrane through the N-terminal domains that interfere with Cargo proteins. Clathrin-coated vesicles bud from multiple organelles and transport proteins between the TGN, endosomes and plasma membrane. Different cargo adaptors function at the multiple donor membranes, such as AP1, AP2 and AP3. The general structure of the AP complexes is composed of an accurately folded domain containing the trunk domains of the two large subunits, which interfere with the membrane, cargo proteins and two unstructured sequence motifs, which are able to attach to clathrin and other accessory proteins

ions (Clapham 2007). The release of Ca2+ ions is triggered upon the induction of the phospholipase C (PLC) by the activation of the G-protein-coupled receptor (GPCR) (Jagannathan et al. 2014a, b), and subsequently, it causes the cleavage of phosphatidylinositol 4,5 bisphosphate (PIP2) into diacylglycerol (DAG) and IP3 that is then able to attach to the IP3R causing a release of Ca2+ ions and thereby a transient elevation in intracellular Ca2+ ion amounts (Clapham 2007). Ryanodine receptors (RyRs) function based on a voltage-induced Ca2+ ion release (CICR), when these receptors bind Ca2+ due to Ca2+ amounts (Martin and Ephrussi 2009). The depolarization of t-tubule membranes may also result in conformational alterations in voltagedependent Ca2+ channels, such as dyhydropyridine receptors (DHPRs), interfering and activating RyRs, which in turn liberate Ca2+ ions (Palacios and Johnston 2001). In addition, Ca2+ ions can either the cytoplasm through ER leakages and they can be pumped back actively into the ER through sarcoendoplasmic reticular Ca2+ ATPases (SERCAs), or they can penetrate from the extracellular surroundings into the cell and contribute to the levels of regulation (Clapham 2007). When ER reservoirs of Ca2+ ions are fast exhausted by IP3 receptor (IP3R)-induced release, a mechanism for Ca2+ entrance into the cell is initiated, referred to as store-operated Ca2+ entry (SOCE) (Clapham 2007; Cui et al. 2013). After luminal Ca2+ ion depletion of the ER, STIM1 proteins accumulate in areas of the ER adjacent to the plasma membrane. In these regions, concentrated STIM1 captures plasma membrane-distributing Orai1 subunits (Loya et al. 2008; Nicchitta et al. 2005) and assemblies them into active Ca2+ release-activated channels (CRAC), which enable the incorporation of

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extracellular Ca2+ into the ER lumen to reestablish Ca2+ levels (Pyhtila et al. 2008; Zhou et al. 2014; Krogh et al. 2001). Curiously, SOCE and the activation of CRAC are not linked to alterations of Ca2+ ion levels in the cytoplasm (Clapham 2007), but rather to the recognition and response to alterations of luminal Ca2+ ion concentrations. Calcium is a widely distributed signaling molecule that can affect various processes, such as the targeting, functional activation and interaction of proteins, entire organelles or nucleic acids. The release of Ca2+ ions may induce a Ca2+ ion front, which is able to propagate across the complete cell (Mueckler and Pitot 1982), and thus, a concentration gradient of Ca2+ ions from the origin of the release or a spatially limited front from bundled channels, which are known as a Ca2+ spark, appears (Mechler and Rabbitts 1981). At the fertilization process after the entry of the sperm, the eventually mostly investigated Ca2+ ion release can be observed (Mueckler and Pitot 1982; Chen et al. 2011), whereas such an release can be detected in a similar manner upon the contraction of muscle, during secretion processes (Clapham 2007) or neuronal processes, such as the release of neurotransmitters for signal transduction (Jagannathan et al. 2014a, b). However, the Ca2+ ion release can also fulfill a prominent role during the reshaping the ER upon cellular signal transduction initiation.

7.5

How Is the Shape and the Function of the ER Regulated?

The ER represents a highly complex organelle that regulates the synthesis of proteins and lipids, controls the calcium level and facilitates the interactions with other cell organelles. The physical structural architecture mirrors the large complexity of the ER. The general architecture of the ER is compromised by a continuous membrane network, which contains the nuclear envelope and the peripheral ER that is characterized by branched tubules and flat sheets. A large number of integral membrane proteins and interactions of the ER with other organelles and the cytoskeleton govern the shape and spread of these ER complexes. These interventions are of a dynamic character and mirror alterations within the cell, either by cell cycle or developmental stage, cellular differentiation, internal cellular signals or interactions with proteins. Although it is widely accepted how the basic forms of ER sheets and tubules are characterized, how alterations in shape or the ratio of sheets to tubules arise as a reaction to distinct cellular stimuli is quite ambiguous. In the following, the assembly of the ER structure, the control of the ER dynamics and the dynamical alterations due to the phase in the cell cycle or specific environmental or cellular cues are discussed. Beyond that, there are instances of how the proteins participating in the design of ER are impacted by these molecular signals, such as calcium release, and how this is mirrored in the dynamics of ER and finally in the function of differentiated cells with varying ratios of sheets to tubules.

7.6

The Effect on Stress on the ER

As previously underlined, the ER is an organelle having many distinct functions that need to be strictly regimented to accomplish the correct functions. The most important function of the ER is doubtless the synthesis of proteins. The accumulation of unfolded or misfolded proteins in the lumen of the ER can still persist despite the presence of multiple chaperones and folding enzymes. When the cell is exposed to this kind of stress, there are a number of issues that must arise to maintain equilibrium and correct function, among them the translational retardation, the break-down of unfolded or incorrectly folded proteins, and an augmentation of chaperone and folding enzyme levels

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to re-establish proper ER and cell function. When the equilibrium is not regained, cell death or apoptosis can occur (Tabas and Ron 2011), thus attaining normal function is crucial for cell survival. When a peptide is targeted for secretion and accessed the lumen of the cell, distinct alterations take place, such as the N-linked glycosylation, the creation of disulfide bonds and subsequently the oligomerization (Braakman and Hebert 2013). The N-linked glycosylation can take place cotranslationally because the protein is transferred to the ER lumen. The oligosaccharyltransferase (OST) is able to alter the Asparagine residue in the Asn-X-Ser/Thr sequence after approximately 13 amino acids of the protein have entered the ER lumen (Nilsson and von Heijne 1993), which elevates the kinetics and improves the thermodynamics values of protein folding (Jitsuhara et al. 2002; Hanson et al. 2009). The misfolding of proteins in the lumen of the ER often arises as a result of the specific luminal conditions and the high protein concentration of newly produced proteins, mature secretion proteins as well as proteins acting as molecular chaperones and folding enzymes. Because of the elevated protein concentration and encapsulation in the lumen, the folding enzymes must logistically first recognize and identify the correct target protein for folding. Failure to properly alter proteins will cause the ER to perceive the deficiency of glucose residues and proteins such as UDP glucoseglycoprotein-glucosyltransferase (UGGT) to reglycosylate the protein (Kaufman 1999; Kaufman et al. 2002; Lee 1992). When the physiological folding process cannot be regained, the hydrophobic residues are still exposed and can interact with Grp78, which leads to the clustering of these proteins and the unfolded protein response (UPR) is switched on (Ron and Walter 2007; Walter and Ron 2011). The first role of the UPR is to raise the ER loading in order to satisfy the requirements of the cell to properly fold the proteins, leading to an extension of the ER through the production of sheets (Schuck et al. 2009) and to an augmentation of the ER folding devices. The UPR comprises three parallel branches that are stress-activated and comprise inositolrequiring enzyme 1 (IRE1) by non-conventional splicing, double-stranded RNA-activated protein kinase R (PKR)-like ER kinase (PERK) by translational guidance by phosphorylation of eIF2a and activating transcription factor 6 (ATF6) through controlled proteolysis (Walter and Ron 2011). In summary, the activation of these three pathways causes the generation of the b-ZIP transcription factors promoting the activation of the UPR genes (Walter and Ron 2011). Firstly, an ER-located protein IRE1, which is a transmembrane endoribonuclease, facilitates the post-transcriptional and non-canonical splicing of the XBP1 mRNA that is targeted to the ER (Yanagitani et al. 2009; Yoshida et al. 2001; Lee et al. 2002) and encodes a transcription factor that drives the upregulating of additional stress sensitive and reactive genes. The nuclease activity of IRE1 is also implicated in the decomposition of a fraction of ER-associated RNAs in a pathway referred to as IRE1-dependent decay (RIDD) (Hollien and Weissman 2006; Gaddam et al. 2013). The cell has developed this mechanism to alleviate the translational burden on ER through the removal of mRNAs that otherwise might be translated and may provide a mechanism for the cell to elevate the stress reaction genes required in UPR. While it is understood that ER stress causes large-scale alterations in the protein and RNA levels of ER, it is still questionable whether this will result in instant restructuring to accommodate the new demands of organelles. Beyond all this, it is uncertain whether the activation of stress-reactive signal pathways induces the alteration of the intrinsic structural elements of ER. Specifically, XBP1 splicing during meiosis has been found to be activated in both Xenopus and budding yeast (Cao et al. 2006; Brar et al. 2012), pointing to a potential link between modifications in ER structure during meiosis and the ER stress response. Both of these are intriguing paths for prospective research examining structural abnormalities in ER triggered directly by cellular signaling features.

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The General Structure and Function of Golgi Apparatus

The Golgi apparatus is the place where the transport and modification of proteins including their processing occur. The proteins processed are produced in the ER and directly transported toward the Golgi apparatus. In specific detail, the proteins can get into the Golgi apparatus at the surface facing the ER, termed cis region of the Golgi and leave the Golgi on the other end of the stack that is next to the plasma membrane of the cell, termed trans-region of the Golgi apparatus (Fig. 7.6). A typical feature of the Golgi apparatus is its highly dynamic nature that is even more prominent in the compartments of the Golgi apparatus, termed cisternae. Another feature of the Golgi apparatus is the wide range of morphologies of the Golgi due to specific cell types in disntinct organisms. However, it is not yet clear whether the different morphologies of the Golgi apparatus affect its function. A question can be raised of how molecules can move through the Golgi apparatus. In concrete terms, the Golgi apparatus possesses multiple different classes of cisterns, which exhibit altered structures, compositions and functions. There is agreement on the total number and the precise definition of these various classes. A promising way to classify the Golgi cisternae is based on the trafficking pathways that facilitate the cisternae import and export events (Day et al. 2013). With this classification parameter, the Golgi cisternae can be subdivided into three major classes based on their individual function and their maturation state. Firstly, the cisternae compromising the cisternal assembly region become COPII vesicles, which are delivered from the ER and commonly contain constituents for recycling processes that need to be transported back to the ER through COPI vesicles. At this individual stage, even new cisternae are produced. Secondly, cisternae involved in the carbohydrate synthesis system can interchange material with each other by way of COPI vesicles. During this stage, the majority of reactions of glycosylation and polysaccharide synthesis take place. Thirdly, cisternae in the carrier generation step manufacture clathrin-coated vesicles and swapping material with endosomes. At this step, biosynthetic load proteins are wrapped around various transport vehicles and the cisternae are subsequently dismantled. Distinct transitions can take place when the cisterna is maturated from one step to another next step. In each step, the construction and the configuration of a cistern can continue to improve, although the distribution routes stay the same. This model affords a coherent and uniform framework for grasping the characteristics of Golgi in different organisms.

Fig. 7.6 The Golgi apparatus alters and sorts proteins trafficking throughout the cell. The Golgi is closely situated in the vicinity of the ER. Protein cargo goes from the ER to the Golgi, where it is transported to multiple targeting sites, such as plasma membrane or lysosomes

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The Golgi apparatus ranks among the most complex organelles inside the cell (Farquhar and Palade 1981). It is made up of flattened membrane bags, the known cisterns, which are usually, but not necessarily, arranged in polarized stacks (Mowbrey and Dacks 2009). According to organism and cell type, a Golgi stack can have up to 3 or up to 20 cisternae (Becker and Melkonian 1996; Mogelsvang et al. 2003; Rambourg and Clermont 1997). Most organisms have single Golgi stacks; however, in many vertebrate cells, the Golgi stacks are connected by lateral links to create a continuous band (Klumperman 2011; Marsh et al. 2001). Even with this varying morphology, the Golgi still works on conserved fundamentals in various eukaryotes (Duden and Schekman 1997; Hawes 2005). Inside a Golgi stack, the cisternae vary in construction, assembly and functionality. Biosynthetic load proteins arrive in the Golgi at the cis end of the staple and exit it at the trans end (Farquhar and Palade 1981). During the transit through the Golgi staple, biosynthetic load proteins experience glycan conversion and various other transformations (Ruiz-May et al. 2012; Stanley 2011). The complex polysaccharides are also made inside the Golgi (Dick et al. 2012; Parsons et al. 2012). The trans-most cisterns are known as tans-Golgi networks (TGNs) and are in charge of wrapping biosynthetic load proteins and polysaccharides in transport containers for shipment to downstream locations (Anitei and Hoflack 2011; Kang et al. 2011; Mellman and Simons 1992; Viotti et al. 2010). The membranes on the cis and trans surfaces of the stack are frequently tubulovesicular patterns that may not be tightly connected to the remaining parts of the stack (Rambourg and Clermont 1997; Staehelin and Kang 2008), but for the sake of clarity, all these patterns will be referred to as cisternae. The functions of the Golgi stack are performed by enzymes and trafficking proteins, which are preferably positioned to a specific group of cisternae. The unstacked Golgi cisternae of the budding yeast Saccharomyces cerevisiae equally exhibit variations in their constitution and functional role (Papanikou and Glick 2009; Preuss et al. 1992). These results gave rise to the concept that the Golgi is composed of several classes of cisternae. However, there is still no consent on the precise number of such classes or the molecular entities underlying them. The easiest route to the classification of the Golgi cisternae is to pay attention to the routes of human trafficking. In this view, cisternae in a certain class share the same mechanisms for importing and exporting their components, and these distribution routes mirror preserved key functions. This approach will enable experimental knowledge of the Golgi organization to be integrated into a wide range of organisms.

7.7.1 Why Are There Various Classes of Golgi Cisternae? In earlier grading regimes, the main factor distinguishing Golgi cisterns was the polarity of the distribution of processing and biosynthetic enzymes. The N-linked oligosaccharides are altered by a series of glycosylation steps driven by Golgi inherent glycosylation enzymes located at distinctive places within the staple (Ruiz-May et al. 2012; Stanley 2011). It was elucidated by density gradient fracturing and immunolocalization analysis of the mammalian Golgi that early reacting glycosylation enzymes generally are clustered in cisterns on the cis side of the staple, whereas late reacting glycosylation enzymes are clustered in cisterns on the trans-side (Dunphy and Rothman 1985; Kornfeld and Kornfeld 1985; Rabouille et al. 1995). In mammalian and plant cells, selected glycosylation enzymes are located directly in the middle of the Golgi staple that is referred to as the medial cisternae (Donohoe et al. 2013; Dunphy and Rothman 1985; Rabouille et al. 1995). The highly ordered, and hence, polarized positioning of glycosylation enzymes is suppose to elevate the efficiency due to the fact that a biosynthetic cargo protein undergoes processing in exactly this ordered

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manner. During another processing type, termed TGN, which is employed by mammalian and fungal cells, the peptidases initiate the activation of prohormones (Rockwell et al. 2002). Why are Golgi enzymes located in distinct cisternae? This separation allows the cell to fine-tune pH, ion content and substrate concentrations for individual sets of enzymes. In addition, the separation of enzymes can be indispensable to avoid two reactions compelling each other. For instance, lysosomal hydrolases of mammals with a mannose 6-phosphate sorting label are altered, and this adaptation must be made before the lysosomal hydrolases collide with Golgi-localized mannosidases (Goldberg and Kornfeld 1983). In plant cells, the segregation of pectic and xyloglucan polysaccharide backbone synthesis reactions to various cisterns impedes the capture of the resulting molecules prior to methyl esterification of the pectic polysaccharides (Atmodjo et al. 2013; Zhang and Staehelin 1992). However, these demands are doubtless unusual, as most carbohydrate synthesis reactions can take place in efficient manner in a unique mixed compartment (Dunphy and Rothman 1985). The more widespread justification for the Golgi cisternae configuration is that it serves as a delay timer (Glick and Malhotra 1998). Biosynthetic freight proteins require a certain amount of time to cross the Golgi, and this retardation offers the possibility to remove resident ER proteins and completely handle cargo proteins (Becker and Melkonian 1996; Rothman 1981). In cells with large Golgi stacks, for that reason, a certain physical and temporal function can be distributed over several cisternae. Further indications for various classes of cisterns stem from morphological investigations of Golgi stacks (Donohoe et al. 2013; Klumperman 2011; Rambourg and Clermont 1997; Staehelin and Kang 2008). In plant cells, electron microscopy showed that the cis most common cisternae tend to stain considerably weaker than the medial and trans-cisternae, and this staining pattern corresponds to the limited localization of glycosylation and biosynthetic enzymes to the medial and trans-cisternae (Donohoe et al. 2013). Beyond that, in the case of numerous organisms, the cisternae on the transsurface are partially or entirely isolated from the Golgi stack (Kang et al. 2011; Mogelsvang et al. 2003; Mollenhauer and Morré 1991; Rambourg and Clermont 1997; Viotti et al. 2010). According to electron tomographic findings, Golgi stacks of mammals exhibit a sharp difference between the transmost cisternae, which manufactures vesicles coated with clathrin, and earlier cisternae, which generate COPI vesicles (Ladinsky et al. 1999; Mogelsvang et al. 2004). The risk exists that advances in analytical methods will lead to ever more fine-grained subdivisions of the Golgi. For instance, a recent report on TGN yeast showed that the GGA and AP-1 clathrin adapters are sequentially enrolled (Daboussi et al. 2012), but a separation of the TGN cistern into two groups after the enrollment of adapters cannot be constructively feasible. In most cases, structural and compository variations among cisterns can masquerade the fundamental functional similarities. Existent Classes of Golgi Cisternae The general consensus is that the Golgi cistern can be grouped into four classes: cis, medial, trans and TGN (Farquhar and Hauri 1997). Each class of cisternae is regarded as the location for a distinct set of processing enzymes. This model is supported experimentally to some degree. For instance, at each step, a trial was conducted on the yeast pro-a factor mating the pheromone precursor, that tracks serial processing by four Golgi enzymes, and the Sec18/NSF vesicle fusion protein, implying that pro-a factor traverses four categories of Golgi cisternae (Brigance et al. 2000). However, studies with mammalian glycosylation enzymes have not resulted in these unambiguous characteristics. As judged by quantitative immunolabeling, individual glycosylation enzymes are most concentrated in particular cisternae, but they have overlapping distributions across the stack (Nilsson et al. 1993; Rabouille et al. 1995). In addition, a particular glycosylation enzyme may have varying intra-Golgi placements in a variety of cell types (Velasco et al. 1993). The polarized disposition of Golgi processing enzymes tends to mirror gradients of enzyme topicalization rather than accurate partitions (Glick et al. 1997).

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Besides the major four possible classes of the Golgi cisternae, mammalian cells possess an additional ER–Golgi intermediate compartment (ERGIC) (Appenzeller-Herzog and Hauri 2006). The ERGIC appears to be stably connected to peripheral ER exit regions; however, it produces mobile membrane carriers, which are transported alongside the microtubules toward the Golgi band (Appenzeller-Herzog and Hauri 2006; Lippincott-Schwartz et al. 2000). These ERGIC-originated membranes can integrate at the cis part of the Golgi stack, where they built an addditional layer that is referred to as the cis-Golgi network (CGN) (Bannykh and Balch 1997; Ladinsky et al. 1999; Mellman and Simons 1992). However, the term CGN can be confusing, since the ERGIC-originated membranes at the cis part of the Golgi stack are not truly a cross-linked scaffold (Ladinsky et al. 1999); hence, these membranes are often termed as the “pre-cis layer.” In plants and algae, the membrane structures, which are referred to as “cis initiators” at the cis region of the Golgi stack, possess morphological and functional characteristics that are similar to those observed in the mammalian ERGIC or synonymously termed in the pre-cis layer (Donohoe et al. 2013). In order to describe the cisternal structure, composition and function of the Golgi in a complete manner, a precise and detailed overall view of the Golgi organization requires to address the following major questions. How can the Golgi cistern transport biosynthetic secretions when material is transferred to one another and even to other organelles? How can the Golgi residual proteins be pinpointed on specific cistern species? What types of trafficking vehicles function on the Golgi? These questions will be investigated in the light of the classification approach offered for Golgi cisterns.

7.7.2 Organization of the Golgi and Models for Golgi-Based Trafficking It is a controversial issue that the properties of the Golgi cisternae are connected to the routes of Golgi membrane traffic (Glick and Luini 2011; Rabouille and Klumperman 2005). At present, the predominant model is cistern maturation, where the Golgi cisternae is postulated to be created de novo and then gradually matures into a TGN cisternae. In the simplest type of the maturation model, a TGN cisterna represents solely an older version of a cis cisterna, and the Golgi seems to be composed of a set of cisternae that mature in a continuous manner (Glick et al. 1997). A more differentiated view is that the ripening process takes place in a series of separate steps, with the various classes of Golgi cisternae constituting sequential stages of ripening (Glick and Nakano 2009). The maturation model can take up most experimental data from a wide range of cell types (Glick and Luini 2011; Rizzo et al. 2013; Staehelin and Kang 2008). Under this model, the residing Golgi proteins can be recirculated from older to younger cisternae and thus remain in the organelle during the progression of the biosynthetic cargo. The mechanism for recycling residential Golgi proteins is still in doubt. One way is to recirculate Golgi membrane proteins in COPI vesicles that emerge from the Golgi cisternae (Donohoe et al. 2007; Glick and Malhotra 1998; Rabouille and Klumperman 2005). There is strong conclusive evidence that COPI vesicles carry at least some Golgi membrane proteins, although contradictory data have been received on the occurrence of glycosylation enzymes in COPI vesicles (Cosson et al. 2002; Gilchrist et al. 2006; Kweon et al. 2004; Malsam et al. 2005; Martínez-Menárguez et al. 2001; Orci et al. 1997, 2000a; Sönnichsen et al. 1998). Transient tubular connections between cisternae were proposed in supplement to the COPI vesicles to facilitate the recirculation of Golgi membrane proteins (Glick and Luini 2011; Marsh et al. 2004; Trucco et al. 2004). Alternative models for Golgi transport consider the cisternae in a completely contrasting light. The fast division model suggests that the Golgi is a continuity structure with glycosylation and export

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taking place across all layers of the stack (Patterson et al. 2008). This model handles the Golgi as one mixed compartment and eliminates the discrimination among the various cisternae. The progenitor and marginal progression models of the cisternal suggest that the Golgi cisternae is a durable structure and that large portions of the cisternae are cleaved and then fused to transfer biosynthetic load, whereas the residual Golgi enzymes stay in the static sections of the cisternae (Lavieu et al. 2013; Pfeffer 2010). These different models for the trafficking at the Golgi seem to indicate that Golgi cisternae can be either transient or static, and either sharply separated or intermixed. Hence, the classification of the Golgi cisternae may be impacted by the individual trafficking model. In the following, it is discussed whether the cisternal maturation model seems to be most suitable for interpretating the experimental results.

7.7.3 Organization of the Golgi with the Positioning of Resident Proteins Specific Golgi cisterns have different habitation proteins; therefore, a classification system ought to consider the mechanisms for identifying the resident Golgi proteins. Localization signs for a series of Golgi glycosylation enzymes have been recognized (Banfield 2011; Machamer 1993). These proteins exhibit a type II morphology including a cytosolic N-terminus and a single transmembrane chain. The transmembrane sequences together with the flanking sequences are frequently essential and adequate for localization in the Golgi region. But the mechanisms of the localization were difficult to grasp. On the basis of the maturation model, glycosylation enzymes are constantly recirculated, probably in a way that is COPI-dependent. The yeast protein Vps74 was involved in the association of specific glycosylation enzymes with COPI (Tu et al. 2008). The size of the transmembrane sequence is relevant for the Golgi regionalization, pointing out that the segmentation into lipid domains is of importance (Sharpe et al. 2010). The length of the transmembrane sequence depending on the intended localization mechanism can encourage or impede the separation into a bended vesicle membrane. It is much more obvious for the specific class of TGN proteins, such as the mammalian TGN38 and the yeast Kex2 (Machamer 1993). In a more detailed way, these proteins feature a type I topology, and they manage to target the TGN through the use of retrieval mechanisms that include adaptor-facilitated detection of signals in the C-terminal cytosolic tails. The separation of the Golgi-based membrane proteins into type II glycosylation enzymes and type I TGN proteins is unequivocal. Conversely, the type I targeting signals of various Golgi glycosylation enzymes appear to be similar (Sharpe et al. 2010). The well-known features of the Golgi localization markers reveal an essential discrimination between the TGN and earlier cisternae.

7.7.4 Organized Golgi and the Trafficking System Trafficking pathways limit the options for the classification of the Golgi cisternae. A trafficking step typically includes: a shell complex that sorts load and culpates vesicles, a regulatory Rab-GTPase, binding proteins and a fusogenic SNARE complex (Bonifacino and Glick 2004). The Golgi system utilizes a restricted amount of these compounds. Arguably, the Golgi-related elements of trafficking can be categorized into three groups, based on their location of operation either at the ER–Golgi intersection, or inside the Golgi, or in transport to and from the TGN. The conserved envelops of the Golgi vesicles are COPI, COPII and clathrin. COPII vesicles transport proteins from the ER into the cis-Golgi and also merge with each other to produce ERGIC

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units (Barlowe and Miller 2013). COPI vesicles have two separate functions: The vesicles reclaim proteins from the Golgi cistern and transport them between the different Golgi cisternae (Barlowe and Miller 2013; Popoff et al. 2011). In plant and algae cells, Golgi-to-ER and intra-Golgi transport activities are performed by various subtypes of COPI vesicles, the so-called COPIa and COPIb vesicles (Donohoe et al. 2007, 2013). COPIa vesicles are restricted to the ER–Golgi boundary, while COPIb vesicles encompass the medial and trans-Golgi cisternae. Mammalian cells include COPI vesicle subtypes, which are similar to COPIa and COPIb (Lanoix et al. 2001; Malsam et al. 2005; Moelleken et al. 2007; Orci et al. 1997). Clathrin-coated vesicles emerge from the TGN to transport proteins to endosomes and lysosomes/vacuoles and can also reclaim proteins from mature to newly produced TGN cisternae (Myers and Payne 2013; Valdivia et al. 2002; Viotti et al. 2010). A rational assumption is that various classes of cisternae are characterized by COPIa, COPIb and clathrin. The amount of Golgi-associated Rab proteins differs according to the cell type. S. cerevisiae represents the most simplest model organism that can be studied since it has a streamlined set of Rab proteins. Ypt1, which is homologous to mammalian Rab1, works together in the ER-to-Golgi transport and additionally in the internal Golgi transport (Barlowe and Miller 2013; Jedd et al. 1995; Suvorova et al. 2002). Ypt6, which is homologous to mammalian Rab6, functions in the transport of membrane carriers from endosomes toward the TGN (Liu and Storrie 2012; Storrie et al. 2012). Subsequently, the Ypt31/Ypt32 pair, which is homologous to mammalian Rab11, fulfills tasks in transport of the TGN toward the surface of the plasma membrane (Jedd et al. 1997). During ER-to-Golgi transport, the conserved binding proteins comprise the coiled-coil protein referred to as Uso1 or p115 and the multi-subunit TRAPP complex (Kang and Staehelin 2008; Lord et al. 2013). The links of the GRASP family are also effective during ER-to-Golgi transportation in mammalian and fungal cells, although they are not present in plant cell (Behnia et al. 2007; Kinseth et al. 2007; Levi et al. 2010; Marra et al. 2001). The retrograde translocation of COPIa vesicles to the ER contains the Dsl1 binding complex (Lerich et al. 2012; Ren et al. 2009; Spang 2012). Intra-Golgi transport mainly occurs to be reliant on the conserved COG complex (Smith and Lupashin 2008; Ungar et al. 2006), despite the involvement of “Golgin” coiled-coil proteins (Faso et al. 2009; Munro 2011). The supply of means of transport from the endosomes to the TGN is carried out with the GARP compound and extra Golgins (Munro 2011; Pfeffer 2011). In conclusion, Golgi-based tethers facilitate the trafficking either between the ER and Golgi, or inside the Golgi, or between endosomes and the TGN. SNARE proteins combine in different permutations to advance the fusion of the membrane. Recent findings point to the fact that in mammalian and yeast cells, one SNARE complex conveys ER-toGolgi anterograde transport, a second SNARE complex transmits Golgi-to-ER retrograde traffic, a third SNARE composite communicates intra-Golgi transport, and one or two SNARE complexes confer retrograde transport to TGN (Malsam and Söllner 2011). The SNARE repository of the Golgi regime is therefore relatively limited (Pelham 1998). With this overview of the traveling mechanism, the concept of classifying the Golgi into cis, medial, trans and TGN classes of cisterns as well as a possibly ERGIC/pre-cis class of layers is not supported, as the number of traveling elements is inadequate. Instead, it is proposed that the Golgi cisternae can be classified into three categories (Mellman and Simons 1992), mirroring three structural and functional phases of maturation: cisternae construction, synthesis of carbohydrates and carrier generation.

7.7.4.1 Stage I: The Cisternal Assembly Membranes in cisternal assembly are equivalent to the ERGIC and pre-cis layers plus one or more cis cisternae in mammalian cells (Farquhar and Hauri 1997), or to the cis initiators with one or more additional cis cisternae in plants and algae (Donohoe et al. 2013). When cisterns are built, the COPII

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vesicles merge homotypically into larger vesicles, which then merge with one another and with extra COPII vesicles to form a fully fledged Cis cisterna (Bentley et al. 2006; Donohoe et al. 2013; Zeuschner et al. 2006). This pattern matches the historical denomination of the Golgi’s cis surface as a “sculpting” surface (Mollenhauer and Whaley 1963). Electron tomographically oriented models of the cis surface of plant Golgi stacks with distinct structures representing successive stages of cisternae arrangement (Donohoe et al. 2013). From these images, it appears that 3–5 COPII vesicles merge to one cis initiator, which then expands by merging additional COPII vesicles to a full-fledged cis cisterna. The synthesis of few carbohydrates takes place in the assembly phase, but the assembling of cisterns is presumably in charge of adding mannose 6-phosphate to lysosomal hydrolases in mammalian cells (Pelham 1988) and constructing flake protein complexes in algae (Donohoe et al. 2013). The capacity to absorb COPII vesicles is the key characteristic of membranes during the assembly period. In the assembly phase, COPIa vesicles are also employed to reuse the components of the transport vehicles in the ER. COPIa vesicle production in mammalian cells appears to be limited to ERGIC (Ladinsky et al. 1999), indicating that the recycling to ER precedes the transport of ERGICbased membranes into the Golgi band.

7.7.4.2 Stage II: The Synthesis of Carbohydrate Cisternae functioning in the carbohydrate synthesis are generally named medial and trans-cisternae. The primary function of these cisternae lies in the glycosylation of proteins and lipids and the synthesis of intricate polysaccharides (Atmodjo et al. 2013; Dick et al. 2012; Stanley 2011). In certain cell types, carbohydrate metabolism is associated with a considerable number of cisternae. Various types of glycosylation enzymes are frequently found concentrated in younger (medial) or older (trans) cisternae, and these distributions often remain a sharp one (Donohoe et al. 2013; Driouich and Staehelin 1997; Dunphy and Rothman 1985). The intercistern transport at the carbohydrate synthesis level is obviously facilitated by COPIb vesicles (Donohoe et al. 2013; Rothman and Wieland 1996; Staehelin and Kang 2008), although the exact function of COPI in intra-Golgi transport is a permanent enigma (Glick and Luini 2011; Rabouille and Klumperman 2005). A number of variants of the maturation paradigm take Golgi polarity into account by assuming that the COPIb vesicles travel in an only retrograde direction (Glick et al. 1997). But a molecular foundation for the unidirectional transport of COPIb vesicles has not been obtained and is difficult to imagine if cisterns all share the same transport equipment during the carbohydrate synthesis phase. An alternative scenario is that COPIb vesicles could percolate by chance throughout the Golgi (Orci et al. 2000b). However, how can the percolation of vesicles enable the concentration of an enzyme in certain cisterna when COPIb vesicles carry glycosylation enzymes? A potential mechanism includes gradual alterations in lipid content. The production of lipids and their restructuring represents an active process within the Golgi (Bankaitis et al. 2012), thereby older cisternae possess an altered content of lipids than younger cisternae. If a certain glycosylation enzyme ends up in a cisterna in which the lipid environment is beneficial for the transmembrane sequence (Sharpe et al. 2010), the enzyme probably will not divide into buds of COPIb vesicles and would therefore accumulate in this cisterna. The concept seems to be highly hypothetical, while it enlightens that stochastic COPIb-facilitated transport may lead to an asymmetric dispersion of Golgi residential proteins. Since older cisternae undergo a transition from the carbohydrate production phase to the carrier assembly phase, they cease their ability to absorb COPIb vesicles. When they still continue to produce COPIb vesicles during the transition step, the cisternae will lose content and hence shrink, as it has been seen in plant cells with electron tomography (Kang et al. 2011). Intermediately, when younger cisterns move from cistern construction to carbohydrate production, they gain the capability to absorb COPI vesicles. Finally, this results in a net material transport from older toward younger cisternae.

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7.7.4.3 Stage III: The Formation of the Carrier Cisternae working on the carrier formation stage can even interact with the TGN (Griffiths and Simons 1986; Mellman and Simons 1992). However, the boundaries of the TGN have examined in various ways that usually associate multiple cisternae at the trans-surface of the staple (Kang et al. 2011; Mogelsvang et al. 2004). To overcome this uncertainty, it is advisable to redefine the phase of carrier building through the ability of a cisterna to wrap biosynthetic cargo in a variety of transport vehicles (Bard and Malhotra 2006; De Matteis and Luini 2008). A certain processing and biosynthesis can also take place during carrier development. For instance, type I TGN proteins are able to initiate the enzymatic break-down of prohormones (Rockwell et al. 2002), and the nearest transsurface cisternae of the staple contain the mammalian sialyltransferase and specific biosynthetic enzymes of the plant’s cell wall (Atmodjo et al. 2013; Rabouille et al. 1995). The assembly of carriers represents a sorting process, which is connected to the condensation of distinct cargo proteins, and to disturbances of the Ca2+ ion concentration, the pH and the lipid content (Bard and Malhotra 2006; Chanat and Huttner 1991; Dettmer et al. 2006; Graham and Burd 2011; Kang et al. 2011; Schmidt and Moore 1995; von Blume et al. 2012). Cisternae be ripped off from Golgi staple during the carrier development phase, and hence, they are free-floating TGN within plant cells and the yeast Pichia pastoris (Bevis et al. 2002; Mogelsvang et al. 2003; Staehelin and Kang 2008). Subsequnetly, the final fate of Golgi cisternae in mammalian cells is not clearly understood, whereas transport vehicles are assembled at the trans-surface of the Golgi that then move toward the plasma membrane (Bard and Malhotra 2006; Polishchuk et al. 2000; Wakana et al. 2012). In plant cells, mature cisternae of carrier formation break down into vesicles and remnant membrane fragments and thus finish the life cycle of a Golgi cisterna (Kang and Staehelin 2008). Cisternae, which are at the step of the carrier assembly, generate constitutive and regulated secretory vesicles targeted to the plasma membrane, and they additionally built clathrin-coated vesicles targeted toward endosomes, lysosomes or vacuoles (De Matteis and Luini 2008). In addition, older carrier forming cisternae can exchange specific compounds with younger carrier forming cisternae through a molecular transduction pathway associated with the clathrin adaptor AP-1 (Valdivia et al. 2002). The creation of clathrin-coated vesicles instead of COPI vesicles is a clear difference between the carrier development phase and earlier phases. Besides, in contrast to earlier cisternae, the carrier forming cisternae interchange membrane with the endosomal membrane system (Pfeffer 2011; Viotti et al. 2010). This disparity is obvious from the reaction of mammalian and plant cells to brefeldin A, which induces cis to fuse with the ER through trans-cisternae, while the TGN merges with endosomes (Klausner et al. 1992; Nebenführ et al. 2002).

7.7.5 The Overall Maturation Versus Micromaturation The Golgi maturation process is characterized as the transition between each of the three phases: cisternal construction, carbohydrate production and carrier generation. All these transitions are characterized by alterations in their trafficking ways. Maturing incidents as visualized by live-cell imaging of yeast probably mirror the phase transition between the carbohydrate production and carrier generation (Losev et al. 2006; Matsuura-Tokita et al. 2006). It is anticipated that the transitions between the phases of the Golgi will be relatively rapid, although some studies on the transitional intermediates will probably provide insights. An appealing scenario is that a cisterna in the phase transition is of a mixed composition (Pfeffer 2010), whereby a domain of the earlier phase diminishes while a domain of the later phase extends. Some residential Golgi proteins are singularly present at a specific stage, while others occur at two or even all three of them. For instance, the yeast-guanine nucleotide exchange factor Gea2 has been

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found on both old (early) and new (late) Golgi cisternae (Chantalat et al. 2004; Spang et al. 2001; Tsai et al. 2013), and the homologous GBF1 protein of the mammals is seemingly connected to Golgi membranes from cisternal assembling to carrier development (Lowery et al. 2013). This coincidence mirrors the interaction between precursor and product in sequential phases between cisternae. Within each step or synonymously termed stage of the Golgi maturation, alterations of the Golgi can be observed during the aging of the cisterna. Examples are given in the following: The first step or stage is termed cisternal assembly step, where the membrane structure increases in size from initially a few fused COPII vesicles to a cisterna of full size. The second step is called the carbohydrate synthesis step, in the course of which the enzymatic and lipidic constitution of the cisternae develops. During the step of the carrier building, the clathrin adaptors are being serially recruited. Alterations at each step of the maturation can be seen as a micromaturation substep. These fairly small alterations deviate mechanistically from one another and from the major alterations in migratory routes that emerge during transitions between phases. This concept forms the foundation for the categorization of the numerous processes that transform the Golgi cisternae. In the end, the three-step model needs to be validated and fine-tuned by examining how the Golgi cisternae ripen. As a possible mechanism for Golgi maturation, a process known as Rab transformation has arisen, which is to some extent rooted in analogy to maturation of endosomes (Glick and Nakano 2009; Mizuno-Yamasaki and Rivera-Molina 2012; Rink et al. 2005). Cascades including the functions of Arf GTPases seem to be crucial for the maturation of the Golgi apparatus (Lowery et al. 2013; Richardson et al. 2012; Stalder and Antonny 2013). The constituents that trigger the transitions between the three steps or stages of the Golgi are supposed to be highly conserved in nearly most of all eukaryotes. Supplementary elements present in specific organisms, such as species-specific Rab proteins, that are linked to the mammal and plant Golgi (Liu and Storrie 2012; Woollard and Moore 2008), are expected to govern the microripening processes and endow the Golgi with its unique characteristics in different cell types.

7.8

Golgi Apparatus and Vesicle-Based Protein Trafficking

Golgi apparatus the first cellular organelle revealed in the year 1898 by an Italian biologist Camillo Golgi. After him, the organelle was named Golgi apparatus. Due to its large size, the Golgi apparatus was investigated easily. Similar to the ER, the Golgi apparatus is associated with the processing, trafficking of proteins that can be either secreted or bound to the membrane. Hence, the ER can be described as the central organelle facilitating the transport of proteins and lipids through the cytoplasm of a eukaryotic cell. An important prerequisite of this organelle is its highly dynamic structure and its broad variety of morphologies in different cell types. The variety is manifested in the Golgi compartments (termed cisternae) being of different morphology due to the cell type. The Golgi apparatus is typically composed of a pancake-like stack of flattened compartments (Fig. 7.6) with possess specific types of enzymes. Since proteins need to be further processed before secretion, such as through the addition and structural remodeling of attached sugars, they seem to transit in an ordered manner through each part of the Golgi stack. The mitochondria are especially conspicuous organelles with a smooth outer surface, which absorb an extensively folded network of internal membrane structures. Mitochondria are the main place of ATP synthesis for cells growing in the vicinity of oxygen, and their intriguing physiology and microstructure is well understood.

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It is not yet understood why there exist these diverse structures of the Golgi apparatus and how the different morphologies impair its function. In order to reveal individual functions of the Golgi apparatus, different Golgi morphologies of different cell types are analyzed in great detail.

7.8.1 How Are Proteins Altered When They Travel Through the Golgi Apparatus? The Golgi processes proteins produced by the ER before distributing them throughout the cell. Proteins initially penetrade the Golgi on the side facing the ER (synonymously termed cis site) and exit the Golgi on the opposite side of the stack, which faces the plasma membrane (synonymously termed trans-site). Proteins need to find their path through the stack of intervening cisternae on which they are altered and packaged for the targeted transport throughout the cell (Fig. 7.6). The cisternae of the Golgi apparatus can be different in total number, shape and organization due to the type of the cell. The commonly employed typical diagrammatic division of the Golgi apparatus into three major cisternae, referred to as cis, medial and trans, is a major simplification of the structural organization and the associated functions of the Golgi apparatus. In some cases, also additional parts can be associated to the Golgi, such as the CGN or the TGN. These Golgi associate networks structures exhibit an increased variable structure, which are compromised by several cisterna-like parts and several vesiculated parts. Individual cisternae or cisternal regions of the Golgi possess a distinct set of protein modifying enzymes. What type of role does these enzymes fulfill? The Golgi enzymes are able to trigger the addition or removement of sugar residues of load proteins (termed glycosylation), the addition of sulfate groups (termed sulfation) and the addition of phosphate groups (termed phosphorylation). The load proteins can be altered by enzymes, which are termed resident enzymes that are typically present in each cisterna. In a serial manner, the enzymes perform appropriately alterations of load proteins. Specific Golgi-facilitated alteration can serve as signals for sorting out proteins directly toward their targeted location inside the cells, such as the lysosome and the plasma membrane. What occurs upon defects in the function of the Golgi apparatus? Defects in several aspects of Golgi function may cause congenital glycosylation disorders, including distinct versions of muscular dystrophy and may promote diabetes, cystic fibrosis and cancer (Ungar 2009). How can load proteins manage to travel between the different Golgi cisternae? There seem to be at least two possible mechanisms: on the one hand, the vesicular transport mechanism and on the other hand the cisternal maturation mechanism. It is important to note that the two models can both provide the Golgi’s balanced steady state and contribute to the processes, although they are largely divers. In 2002, it has been revealed that the membrane system and the vesicles facilitate the secretion mechanisms inside cells. There is strong evidence that general molecules and processes are employed during the fusion and fission of the membrane in eukaryotic cells. Moreover, it has been shown that biochemically reconstituted Golgi membranes of mammalian cells and isolated vesicles are able to transmigrate from one cisterna to the adjacent cisterna. In a vastly different approach, by employing yeast genetics, crucial proteins in the secretion pathway had been enlightened and precisely characterized in their function during the entire secretion process. These identified molecules associated in the generation and fusion of vesicles formed the basics for the vesicular transport model. There exist currently two models how protein trafficking through the Golgi apparatus can be performed, the cisternal maturation model and the vesicle vehicle model (Fig. 7.7). The first model deals with the movement of proteins through the Golgi and is referred to as cisternal maturation model. When a new cis cisterna is created, it will transmigrate through the entire Golgi stack, alter itself by maturation, when it is clustered medial, and finally, the trans-enzymes can be moved by

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Fig. 7.7 The cisternal maturation model and the vesicle vehicle model

vesicle carriers from later to earlier cisternae that is termed retrograde trafficking. The second model, which is named vesicular transport model, deals with cisterna that stay at their position and contain unaltered enzyme content. Instead the proteins are transported through the Golgi staple via vesicles moving from earlier to later cisternae that is referred to as anterograde trafficking. What Is the Evidence for a Vesicular Transport Model? A principal observation was that the vesicles created inside the Golgi can transport load proteins between the cisternae from the cis side toward the trans-side. These findings clearly point out to the vesicular transport model (Farquhar and Palade 1998). The vesicular transport model states that the Golgi cisternae represent stable regions that contain distinct protein modifying enzymes, which can add or cut off sugars, add sulfate groups and fulfill other types of post-translational modifications. Vesicles reaching at an individual cisterna possess loaded proteins, which can be altered by the resident enzymes concentrated inside the cisterna. The next step represents the budding of new vesicles that possess the load proteins and mange the transport toward the next stable cisterna, in which another set of enzymes can process and alter the cargo protein (Rothman and Wieland 1996).

7.8.2 The Cisternal Maturation Model Before the enlightening findings that vesicles contain proteins to be transported toward the Golgi cisternae, it has been hypothesized that each Golgi cisterna was rather transient and that the cisternae themselves travelling from the cis toward the trans-surface of the Golgi can be altered over time. The protein movement as passengers of cisternae through the Golgi staple is termed the cisternal

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maturation model. This model states that the enzymes located in an individual cisterna can be altered over time, whereas the cargo proteins still remain within the cisterna. Once this model was largely supported, it has been revealed that there are large numbers of small transport vesicles nearby the Golgi, and hence, the vesicular transport model has been proposed that seems to replace the old model by a novel updated model. However, it can even be revisited an old idea that can become important in a new manner. What Type of Novel Evidence Exists for the Cisternal Maturation Model? In the 1990s, multiple cell types have been analyzed to widen the knowledge of the Golgi apparatus. Using mammalian cells, it has been shown in vitro how large protein complexes are travelling through the Golgi. With immunoelectron microscopy, it has been revealed that rigid, 300 nm wide, rod-shaped, procollagen trimers travel through the Golgi apparatus of mammalian fibroblasts. Moroever, it has been reported that procollagen can solely been found in Golgi cisternae, whereas it is absent in vesicles that usually possess a much smaller diameter, even less than 100 nm, and hence, these vesicles are smaller in diameter that the diameter of procollagen (Bonfanti et al. 1998). Beyond that, similar results have been found in the Golgi apparatus of algae. Flagellated protists of vastly different types built and export scales, which are in contact with the cell membrane of these organisms. In specific detail, the scales seem to be different, but possess a defined size and morphology. In various species of algae exporting scales of relatively large size (1.5–2 mm) and even moderately sized (about 40 nm), the scales can be continuously detected in the Golgi, while there are not found within transport vesicles (Becker et al. 1995; Becker and Melkonian 1996). Finally, these findings of various cell types point out to the cisternal maturation model for the transport of proteins across the Golgi apparatus. What roles fulfill all the vesicles that have been identified inside the Golgi? The favored cisternal maturation model states that the vesicles serve as transport machinery for enzymes of the Golgi rather than for other protein load. The so-called retrograde vesicles can move load backward from the Golgi budding site toward the cisterna to deliver enzymes toward younger cisternae. Thereby other vesicles, which came from older cisternae, transfer the enzymes required for the following next steps of the post-translational alteration of proteins (Glick and Malhotra 1998; Pelham 1998).

7.8.3 Which Model Is Best Suited for the Precise Description of the Golgi Functions? There is a large agreement that the cisternal maturation model is the favorite candidate (Emr et al. 2009). There has been experimental support that strongly underpins the process of the cisternal maturation. Live-cell fluorescence microscopy has been employed to visualize the cisternal maturation in the Golgi apparatus of living S. cerevisiae yeast cells (Losev et al. 2006; Matsuura-Tokita et al. 2006; Malhotra and Mayor 2006). The Golgi apparatus of S. cerevisiae possesses a specific structural lack, since the Golgi stack has not the typical pita bread appearance and is instead rather less organized. In specific detail, the cisternae are distributed in an individual and irregular manner throughout the entire cell. Based on this unusual structure, it is well suited for light microscopic analysis of alterations in individual cisternae on different time scales. In contrast, the vesicular transport model states that each individual cis cisterna stays a cis cisterna, which contains specific cis enzymes throughout its whole lifespan. Conversly, the cisternal maturation model states that a newly created cis cisterna can possibly maturate toward a medial and at a

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later time point toward a trans-cisterna, before it breaks apart upon packaging of the entire content for the distribution to their final destinations inside the cell. In order to reveal what type of model predicts the experimental results appropriately, green and red fluorescent proteins are coupled to proteins, which are located in different, individual cisternae of S. cerevisiae, and these fluorescently labeled proteins are tracked over specific time spans. The experiments are planned to either probe the vesicular transport model or the cisternal maturation model. When the vesicular transport model is applicable, the cisternae will stably possess the same fluorescently labeled Golgi proteins on different time scales. Conversly, when the cisternal maturation model is not applicable, every cisterna will display a different set of Golgi proteins on different time scales. In fact, it has been seen that individual cisternae altered their composition of fluorescently labeled proteins on different time scales. These findings supported pronouncedly the cisternal maturation model.

7.8.4 What Are the Major Questions About the Trafficking of Proteins Through the Golgi Apparatus? There is substantial agreement that the cisternal maturation model is well suited to describe the experimental results. However, it is not yet fully understood whether all cargo proteins use the same pathway. The dynamics of cellular membrane proteins, such as Golgi membrane proteins, can be analyzed quantitatively and it has been demonstrated that some cargo proteins can move through the Golgi more slowly than the rates with which the cisternae mature (Patterson et al. 2008). Thus, these results lead to the conclusion that the cisternal maturation model cannot accurately describe the experimental results. Since the cisternal maturation model is not applicable in all cases, an additional model is proposed, in which a two-phase system of membranes defines what kind of cargo proteins and Golgi enzymes need to be dispersed during their transport. In order to be more precise, specific cell types possess linkages between different cisternae in a single Golgi stack, such as between between cis and medial cisternae. For instance, intercisternal connections have been identified during the propagating waves of protein traffic within mammalian cells (Trucco et al. 2004). Therefore, an advanced and improved version of the cisternal maturation model is required. Distinct aspects of the protein transport machinery over the Golgi are clearly revealed, while there is still less knowledge about the specifics of the Golgi within various other organisms. There is a question that remains unanswered of whether there are universal characteristics in all Golgi. There are selected important question about the Golgi to be raised for future ongoing research: Can various types of secretory load employ the same routes through the Golgi staple or are different routes developed? What type of molecular mechanisms facilitate and control the maturation process of the Golgi cisternae? Do specific specialized regions within the Golgi cisternae exist? How are these special regions generated and what particular roles do they fulfill during the sorting of cargo and its export? How are the individual compartments of the Golgi compartments designed and restructured? Is stacking architecture of the Golgi crucial for the membrane trafficking? If the answer is yes, how can organisms such as S. cerevisiae circumvent this demand? (Emr et al. 2009) In conclusion, the structural architecture of the Golgi apparatus can be different due to the individual cell type. In the yeast S. cerevisiae, based on the vastly distributed feature of the Golgi cisternae, individual one can be enrolled. Using fluorescently labeled proteins targeting different

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cisternae, it has been evidenced that the Golgi cisternae can alter with time, which underpins the famous cisternal maturation model for the protein traveling across the Golgi apparatus. However, in the end, there is much not yet enlightened about the structure and function of the Golgi.

7.9

The Interface Between Endoplasmatic Reticulum and Golgi Serves for Protein Sorting

The trafficking of proteins is crucial for the physiological cell functions under normal healthy conditions. In eukaryotic cells, the spherical transport vesicles can deliver proteins and lipids that are located in one internal membrane-bound compartment toward another compartment associated with the secretory pathway. When an individual protein is transported to a defined target region, which is commonly termed protein sorting, it represents an important function that is inherently connected to the biogenesis of vesicles (Gomez-Navarro and Miller 2016). The principles of the sorting of cargo by the vesicle traffic machinery are presented and it is discussed how these various mechanisms that transport the cargo proteins are selected and how they are subdivided into distinct transport vesicles. A major focus of current research lies on the secretory process and especially on the two initial compartments: the ER and the Golgi apparatus. Emphasis is placed on the great complexity and the diverse function and precise regulation of the cargo adaptors, which are presented in compressed form, with a focus on the mechanistic insight into the protein sorting process in living cells. Since eukaryotic cells are known to be highly compartmentalized, they contain distinct organelles with specific protein and lipid arrangements. Due to the continuous exchange of membranes and cargo proteins during the highly dynamic protein trafficking, the physically linked compartments of the entire secretory pathway are remodeled. In specific detail, roughly one-fourth to one-third of the cell’s proteome seems to be associated with the secretion into extracellular microenvironment or with the interior membrane system. The first step of the secretory signal transduction pathway represents the ER-to-Golgi transport. Inside the ER, the proteins targeted toward the extracellular microenvironment or to other organelles along their transport way they are packaged into specific types of vesicles that deliver them toward the Golgi apparatus. During this stage, all cells may be able to identify them as native and non-native proteins, which subsequently leads exclusively to the transport progression of appropriately folded and assembled cargo proteins. Within the export to the ER, a majority of secretory proteins is actively sorted. Conversely, trafficking of proteins can alternatively be performed without a selection, which is termed bulk flow. In the end, the transport from Golgi to ER contributes to the efficient return of immature charges or even escaped ER-resident proteins to the ER. In the following, it will be presented how cells fulfill the sorting requirements of the largely diverse set of proteins that step over the ER–Golgi interface, which requires a huge cellular effort due to the vastly heterogenous cargo proteins.

7.9.1 What Are the Underlying Principles of Selective Capture by Vesicles? The guided transport of proteins among organelles of the secretory pathway is performed by spherical membrane-bounded vesicles, which extent as a bud from a donor organelle carrying the transport protein and fuse with an acceptor organelle in different part inside the cell. The fission and fusion transport mechanisms enable secretory proteins to overcome the membrane barriers, which are thus not disturbed in their specific functional segregation by organelles. The specific classes of transport vesicles are generated by conserved sets of cytoplasmic proteins, which are grouped based on their protein coating that facilitate their assembly. There exist basically three major vesicular coating

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proteins that can be detected across all eukaryotic life, which are clathrin, COPI and COPII proteins derived from evolutionarily closely resembling envelope proteins. In specific detail, COPII-coated vesicles deliver cargo proteins of the ER toward the Golgi. In contrast, COPI-coated vesicles can deliver cargo in the opposite and hence retrograde direction, which means from the cis-Golgi site back toward the ER, and also inbetween different Golgi cisternae. Moreover, clathrin-coated vesicles are built by the plasma membrane and the TGN in order to provide the fusion with endosomes or lysosomes (Fig. 7.5). The coatings of the vesicles fulfill two important tasks: firstly, they bend the vesicle membrane into a spherical object and secondly, they fill the vesicle with distinct cargo protein. By the coupling of the cargo selection with the formation of the vesicle, the cells manage to efficiently sort the proteins as an inherent feature of the specific transport route.

7.9.2 Recognition of Sorting Signal by Coat Adaptors The analysis of the cell surface receptor internalization through clathrin-based endocytosis first revealed the principle that distinct protein-based signals facilitate the targeting of specific cargo to vesicles. The biochemical, structural and genetic discrimination of clathrin and other related vesicle types has identified how these different envelope configurations link the sorting of cargo with the overall assembly of vesicles. A key feature of the appropriate sorting of cargo represents specific coat subunits (synonymously termed cargo adaptors) that contain binding surfaces for the recognition of specific sorting signals, which are located within the cytoplasmic domains of cargo proteins. The interplay between the coating of the vesicle and the signal triggers the engulfment of cargo by the forming vesicles. The majority of the binary cargo–coat interactions determined in vitro have a relatively low affinity, which can be relevant with respect to coat dynamics in trafficking. During the entire vesicle lifetime, the envelope proteins can be detached from its membrane surface in order to uncover the fusion machinery. Hence, the linkages between the enveloping proteins and the components of the vesicle need to be reversible. Nevertheless, cargo adapters frequently also exhibit an affinity to lipids, which in turn leads to both specific recruitment and overall affinity of the adapter to a donor organelle. Consequently, adaptors are able to interact directly with structural scaffolding components of the numerous coatings, which thereby aid in connecting the membrane, cargo proteins and the membrane deformation system. Knowledge of how the local cooperative interplay between cargo proteins, membrane lipids, adaptor proteins and scaffolding proteins can mutually contribute to affect the assembly of vesicles is a key determinant for nearly all cellular transports.

7.9.3 Coupling of Cargo and Coat by Cargo Receptors A limitation of the signal-facilitated sorting of the coat-linked cargo adaptors is the accessibility of the signal. The numerous soluble secreted and lysosomal proteins cannot therefore interact directly with the subunits of the vesicle envelope, since the barrier formed by the lipid bilayer is in the way. The sorting of these cargo proteins is performed by a receptor-facilitated transport. The cargo receptors represent proteins, which are distributed over the entire membrane and bind simultaneously to cargo proteins and coat adaptor proteins in order to efficiently target soluble proteins toward nascent vesicles (Dancourt and Barlowe 2010; Geva and Schuldiner 2014; Guo et al. 2014; Barlowe and Helenius 2016). A key feature of the function of cargo receptors is their capacity to bind cargo in a particular compartment and release it by fusion with the downstream organelle. Thereby, the cargo receptor can be reused by returning it to the donor compartment to facilitate future trafficking. Cargo receptors are long-lived proteins that facilitate several rounds of transport events. However, it is still

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difficult to understand how the majority of charge receptors manage the direction of vector movement between the compartments. One of the most characterized examples is the KDEL receptor, whose name is based on the conserved amino acid sequence Lys-Asp-Glu-Leu (KDEL) serving as an ER retrieval signal and that can cycle between the ER and Golgi in order to bring escaped ER resident proteins back. The binding and release of the ligand depends on the pH. In specific detail, in the lower-pH environment of the cis-Golgi, the receptor can interact with the retrieval motif, whereas in the neutral pH of the ER lumen, this interaction is impaired. Moreover, for other receptor-cargo interaction pairs, additional factors of the lumenal enivornment, such as ion concentration, can impact the conditional interaction. In addition, there seems to be a cargo-based alteration of the localization of the KDEL receptor (Lewis and Pelham 1992). These results indicate that the KDEL receptor represents not solely an inert platform but can also react to cargo sorting requirements that may be facilitated by allosteric interactions of the cargo binding. While integral membrane cargo proteins are in principle capable of interacting directly with envelope adapters and therefore are not supposed to require cargo receptors, several examples of such proteins with cargo receptors have been reported in several different routes. These membrane cargo receptors seem to provide a sorting signal that is not present in the cargo, but they may additionally fulfill functions in chaperoning transmembrane domains, such as Erv14/CNIH, and the identification or recovery of unassembled components of oligomeric complexes, such as Rer1. It is still not yet clear how the sensing and clearance of membrane-embedded interactions is regulated and coordinated in each cellular compartment.

7.9.4 Sorting Mechanisms at the ER–Golgi Connection Site 7.9.4.1 How Is the Cargo Recruited into COPII Vesicles? What Initiates the ER Export? The entry of the nascent proteins in the secretory mechanism is triggered by the decisive incorporation of precisely folded and assembled secretory and membrane proteins into vesicles that are generated by the cytoplasmic COPII coating. The coating consists of five subunits, including Sar1– GTP, dimeric Sec23/Sec24 and tetrameric Sec13/Sec31 (Barlowe et al. 1994). The budding process of COPII vesicles is triggered when Sar1–GTP starts to direct Sec23/24 to the ER in order to create a so-called prebudding complex to which at a later time point Sec13/31 is recruited through the interaction of Sec23/Sar1 with Sec31 (Bi et al. 2007). Sec13/31 facilitates the bending of the membrane and creates a cage-like external coat of the nascent vesicle (Stagg et al. 2006; Fath et al. 2007; Bhattacharya et al. 2012), whereas Sec23/24 drives the specific selection of the cargo proteins. The assembly of the COPII coat proteins takes place at specific membrane regions, which are referred to as ER exit sites (ERESs) (Lee et al. 2004; Sato and Nakano 2007). The cargo proteins are able to accumulate temporarily at specific sites, such as ERESs, before they are embedded in a nascent vesicle or accompany the embedding. There is a certain degree of quality control mechanisms in ERESs, since ER residents and misfolded cargo proteins, such as the thermosensitive vesicular stomatitis virus G-protein, are specifically excluded from ERESs (Mezzacasa and Helenius 2002). The Sec24 subunit represents a cargo adaptor protein of the COPII coating (Miller et al. 2002). Genetic, biochemical and structural experimental analysis revealed a specific and precisely defined set of cargo–Sec24 interactions that seem to form the basis for the cargo specificity in the assembly of COPII vesicles (Miller et al. 2003; Mossessova et al. 2003; Mancias and Goldberg 2007, 2008; Pagant et al. 2015). Yeast Sec24 possesses at least four specific cargo-binding sites, each of which identifies different sorting signals and thus broadens the range of cargo that can be processed with the same type of vesicle. Mammalian Sec24 has an additional cargo-binding site, which is characterized by its distinct structure and recognizes even another signal (Mancias and Goldberg 2008). More

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diversity of the cargo is provided by the expression of several isoforms of the Sec24 subunits. For instance, there are two isoforms of Sec24 expressed in yeasts, such as Iss1/Sfb2 and Lst1/Sfb3 and four isoforms of Sec24 expressed in mammals, such as Sec24A, Sec24B, Sec24C and Sec24D. Each of them possesses specific preferences of distinct cargo proteins. Besides an expansion of the cargo repertoire, various Sec24 isoforms can also affect the structural features of the vesicle assembly. The yeast Sec24 paralogue Lst1/Sfb3 is specific for the envelope of the large oligomeric protein Pma1 and thus seems to form larger vesicles than pure Sec24 vesicles (Roberg et al. 1999; Kurihara et al. 2000; Shimoni et al. 2000). In fact, vesicles can be preprogrammed to obtain a specific geometry that is determined by the distinct combinations of coat adaptors and suitable cargo proteins. From the viewpoint of the cargo protein, the recruitment of the appropriate type of coat adaptor, the cargo protein itself can impact the vesicle’s geometry. The sorting signals sensed by Sec24 cover two simple amino acid motifs, such as the DxE or LxxL/M/E motifs in yeast Bet1, Sys1, and Sed5 proteins, and folded epitopes, such as the longin/SNARE domain in the Sec22 protein (Mossessova et al. 2003; Liu et al. 2004; Mancias and Goldberg 2007). However, it is still unknown how many different sorting signals of a specific Sec24 molecule can interact at the same time. The major knowledge of the structural cargo binding by Sec24 has been obtained from cocrystalization studies of coat proteins, which are bound to synthetic peptides that comprise the sorting signal. In addition, the full impact that every cargo protein has on interacting surface with Sec24 is still elusive. In specific detail, sec24 represents a cargo-binding surface, which is not allosterically alterating its conformation upon the binding of cargo proteins. In this aspect, the COPII vesicle transport machinery is dissimilar to the clathrin cargo protein adaptor complexes that alter their conformation due to the binding of the cargo protein and thereby induces the formation of the entire vesicle. In contrast, the initiation of COPII vesicle formation is triggered by accessory proteins, which are not necessarily components of the vesicle coating.

7.9.4.2 How Is the Cargo Enveloped by COPI Vesicles? What Delivers the Cargo from the Golgi to the ER? COPI vesicles manage the recovery of lost ER resident proteins and transport components that continuously translocate from ER to Golgi and vice versa. The COPI complex, referred to as coatomer, is composed of seven subunits, such as a, b, b′, c, d, e, and f-COP, which are targeted toward membranes as an intact assembly (Hara-Kuge et al. 1994). In specific detail, based on biochemical features, the coatomer is subdivided into two major subcomplexes: firstly, as a trimer that is consistant of a, b′, and e, referred to as the B-subcomplex and secondly, a tetramer that encompasses b, c, d, and f and refers to the F-subcomplex (Waters et al. 1991; Eugster et al. 2000). Based on bioinformatic and crystallographic analyses, it has been shown that the COPI coat displays structural motifs, which are resembling those in the clathrin and COPII coats, such as the extended a-helical solenoid domains connected with b-propeller structures (Devos et al. 2004; Lee and Goldberg 2010). In contrast, the structural assembly of these various elements differs between the coat systems. Although the tetrameric F-subcomplex is similar to the cargo-binding clathrin adaptor protein (AP) complexes, the structural analysis revealed clearly identifiable b-propeller domains of a-COPI and b′-COP that serves as interaction region with dilysine motifs, which represent the predominant retrieval sign for nearly all ER resident membrane proteins (Jackson et al. 2012). Moreover, the Fsubcomplex seem to fulfills tasks in the interaction with the cargo, in which the b- and d-subunits are crucial for sorting of Arg-driven retrieval signals (Michelsen et al. 2007) and the aromatic amino acids within the small cytoplasmic tails of the p24 family proteins can interact directly with the Fsubcomplex (Fiedler et al. 1996). Additionally, the d-COP can bind to a WxW motif of the cytosolic accessory protein Dsl1, although this interaction domain is located distal to the surface of the membrane (Suckling et al. 2015). However, there is still the possibility for additional cargo-binding

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surfaces to be recognized on the F-subcomplex. In an alternative manner, the protein–protein interaction sites on the COPI AP-like complex seem to provide specific tasks in coat regulation that are beyond that of simple cargo binding. The knowledge about the arrangement of COPI subunits inside the assembled coat was gained by cryo-electron tomography of COPI-coated vesicles and the general fitting of crystal structures into the EM density (Dodonova et al. 2015). The cargo-binding site surfaces of an a- and b′-COP dimer were facing the membrane and lie in close proximity. In specific detail, their a-solenoid domains built an arch-like structure that is orientated away from the membrane and seem to provide the membrane scaffolding, when these structures are connected through the F-subcomplex. A tetramer, which is composed of c-f-COP and b-d-COP dimmers, can additionally built a hinge-like structure that is analogous to that of clathrin AP complexes, but exhibits a more extended conformational shape. The importance of the different structures of the COPI F-subcomplex and the AP complexes seems to be clearly demonstrated. However, there may another explanation for this difference that is based on the cargo coupling to the vesicle formation. In specific detail, the clathrin adaptors seem to expose a “closed” conformation within the cytosol that can turn into an “open” conformation due to the binding to the membrane, which thereby leads to the exposure of clathrin-binding sites to initiate the assembly of a vesicle (Jackson et al. 2010; Kelly et al. 2014). Subsequnetly, the assembly of vesicles in the clathrin system is mechanistically associated with the binding of the cargo. In fact, when an adaptor complex is connected to its ligand, the assembly of the vesicle is exclusively further progressed. Conversly, the COPI structure, which has been built in the absence of cargo proteins and in the presence of a lipid bilayer, represents a “hyperopen” conformation (Dodonova et al. 2015). However, it is still elusive how these putative structural rearrangements facilitate the binding of cargo, membrane binding and the assembly of the vesicle coat, whereas it seems to be based on allosteric effects that impact the assembly of COPI.

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What Mechanistic Roles Fulfill Cargo Receptors in Sorting Processes?

7.10.1 Cargo Receptors Provide ER Export There exists a high diversity among the cargo proteins, which can traverse the entire ER, when they are soluble proteins and may also depend on cargo receptors to interfere with the COPII coat. There are many review articles about the ER export receptors that present and discuss the diversity of these trafficking factors (Dancourt and Barlowe 2010; Geva and Schuldiner 2014; Barlowe and Helenius 2016). The receptor-driven ER export enables the transport of a wide variety of cargo proteins, which can be targeted toward vesicles through the utilization of distinct adaptors, receptors and accessory factors (Geva and Schuldiner 2014). Another important feature of the receptor-driven export represents the precise regulation of the transport due to the degree of protein folding. When a cargo receptor solely interacts with its ligand, which therefore needs to be folded in a correct manner, hence only mature secretory proteins will be exclusively transported and depart from the chaperone-rich lumen of the ER. Mechanistic insights into how certain cargo receptors identify their cargo proteins are still increasing, and there is little knowledge as to whether the binding of client proteins triggers allosteric alterations that then induce envelope binding, thereby guaranteeing that only cargo-loaded receptors are transported. The properly most investigated mammalian ER export receptor is the ERGIC-53/LMAN1. In fact, the ERGIC-53 represents a single-pass transmembrane protein (Schweizer et al. 1988; Schindler et al. 1993), which possesses a large N-terminal lumenal lectin domain for its interaction with glycosylated cargo proteins (Appenzeller et al. 1999). The cathepsin Z–related glycoprotein can interact with the

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ERGIC-53 protein in the ER and it can be released at later time points in the ERGIC by utilizing a well-established pH- and calcium ion-facilitated mechanism (Appenzeller-Herzog et al. 2004). ERGIC-53 additionally transports secreted serum coagulation factors and disorders of human blood clotting are lined to specific mutations in the ERGIC-53 (Nichols et al. 1998). ERGL, VIPL and VIP36 represent related lectins with specific intracellular localizations within the secretory pathway, also interacting with different glycoproteins in a Ca2+ ion-, pH- and sugar-dependent fashion (Kamiya et al. 2008). Another role of ERGIC-53 in protein assembly has been supposed to be in the process of the oligomerization of IgM molecules. ERGIC-53 fulfills the tasks of an export receptor for fully assembled IgM “monomers,” which contain two heavy and two light chains that are connected through disulfide bonds (Anelli et al. 2007). The oligomerization of these subunits to polymeric IgM is mediated by ERGIC-53, which may be facilitated by the oligomeric state of ERGIC-53 itself, building finally a hexamer. In guiding the trafficking of different sets of unrelated glycoproteins, ERGIC-53 can employ of accessory proteins help to improve the specificity for certain clients, such as ERp44 that aids in exporting IgM and MCFD2 that triggers the interaction with coagulation factors (Cortini and Sitia 2010). Through the use of accessory factors, the diversity of customers served by an individual cargo receptor can be increased. Cargo receptors are able to facilitate the interplay between transmembrane cargo proteins and coat proteins. Under certain conditions, these transmembrane cargos do not have their own ER export motif and therefore require a receptor that binds to the coat. That is the specific case with lipidanchored proteins facing the ER lumen. After their synthesis and integration into the ER, the glycosylphosphatidylinositol (GPI)-anchored proteins are embedded in the ER membrane by a distinct lipid moiety. Hence, they miss cytosolic domains for the interaction with the COPII coat. Instead, they need the support of a receptor family referred to as the p24 family. Subsequnetly, these abundant oligomeric proteins are able to shuttle between the ER and ERGIC/cis-Golgi through their efficient incorporation into both types of vesicles, such as COPI and COPII vesicles (Wada et al. 1991; Stamnes et al. 1995; Belden and Barlowe 1996; Blum et al. 1996; Emery et al. 2000; Gommel et al. 2001; Langhans et al. 2008). The p24 proteins have been conserved across all eukaryotes and all possess a stereotypical organization of their domains: a lumenal N-terminal GOLD (Golgi dynamics) domain, a coiled-coil region, a single transmembrane domain and a short cytoplasmic tail, which can interact and support the nucleation of both COPI and COPII elements (Fiedler et al. 1996; Goldberg 2000; Belden and Barlowe 2001; Anantharaman and Aravind 2002; Strating et al. 2009). While the molecular features of cargo detection are still not known, p24 proteins appear to act as lectin in the detection of the carbohydrate fraction of the GPI anchor (Manzano-Lopez et al. 2015). Since the cargo does not come into contact with the receptor complex until the GPI binding has been rearranged accordingly, the p24 family also appears to act as an indicator for protein maturation. Certain plasma membrane proteins have a need for a cargo receptor, although they carry their own ER export motifs. An efficient ER export in this situation demands several signals: one signal is found in the cargo protein itself, a second signal originates from a cargo receptor. The best-characterized examples are the yeast Erv14, the cornichon in flies and the CNIH in mammals, which act as receptors for various plasma membrane resident proteins (Powers and Barlowe 1998, 2002; Nakanishi et al. 2007; Herzig et al. 2012; Pagant et al. 2015). Erv14 probably fulfills several functions in its role as cargo receptor. First, it offers affinity to the COPII mantle through a cytoplasmic loop. This function is necessary for all its cargo customers. In multiple instances, this Erv14 signal enhances the interplay between Sec24 and endogenous sorting signals of cargo proteins, thus improving the affinity of the envelope for Erv14-bound cargo (Powers and Barlowe 2002; Pagant et al. 2015). Second, Erv14 can chaperonise long transmembrane domains typical of plasma membrane proteins. In fact, the Erv14 reliance for ER export is associated with the length of the transmembrane domain (Herzig et al. 2012). Ultimately, based on the interaction of Erv14 with a specific

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subset of cargo through a binding site embedded in the lipid bilayer, it appears to act as a quality controller, since it only binds when the transmembrane domains of the cargo protein are correctly assembled, thereby linking the COPII recruitment with the protein folding (Pagant et al. 2015).

7.10.2 Cargo Receptors Act in the Retrograde COPI Trafficking The receptor that facilitates the Golgi–ER retrieval of ER-resident proteins represents one of the first initially identified cargo-binding receptors acting in protein trafficking. The yeast Erd2 is able to interact with the tetrapeptide motifs, such as KDEL (Lys-Asp-Glu-Leu) or HDEL (His-Asp-Glu-Leu), which have been identified on ER residents (Semenza et al. 1990). In humans, there are three homologs of the receptor, such as ERD21, ERD22 and ERD23, each of which seem to possess slightly distinct preferences for specific substrates (Raykhel et al. 2007). All of which represent proteins that possess seven transmembrane domains to directly interfere with both cargo proteins and COPI components (Majoul et al. 2001). The association with cargo proteins and their release through KDEL receptors is managed through the differences in the luminal pH of the Golgi and the ER. In specific detail, the lower-pH milieu induces the binding to the cargo, whereas the increased pH milieu supports the release of the cargo proteins (Scheel and Pelham 1996). A different soluble cargoreceptor complex that targets ER-resident proteins deficient in known sorting signals is the Erv41Erv46 complex. Erv41 and Erv46 are closely related proteins comprising two transmembrane domains, each with a large lumenal domain. The Erv41-Erv46 complex is known to interfere with cargo proteins such as Fpr2 and Gls1 in a pH-dependent fashion comparable to the KDEL receptors (Shibuya et al. 2015). Similar to the anterograde transport, not all membrane proteins to be retrieved carry their individual sorting signal. The ER retrieval receptor Rer1 facilitates the trafficking between the Golgi and the ER of a vastly variable group of ER membrane proteins lacking both KKXX and K/HDEL signals (Sato et al. 1997). In yeast, these cargos encompass resident ER system proteins, such as Sec12, Sed4, Mns1, Sec71 and Sec63, as well as monomeric subunits of complexes, which are commonly needed for the forward trafficking (Sato et al. 2003, 2004; Kaether et al. 2007; Valkova et al. 2011). Among the latter proteins is Rer1 that can interact with a polar residue of the transmembrane domain, which is generally hidden within the assembled complex (Sato et al. 2003). Similarly, human Rer1 can function as an ER retrieval receptor (Füllekrug et al. 1997). Consequently, the ER retrieval utilizes accessory proteins to support and/or expand the binding of cargo. The soluble cytoplasmic Vps74 protein functions as a vesicle loading protein of glycosyl transferases, since it can interact with a semi-conserved motif (F/L-L/V/I-X-X-RK) of their cytosolic tail and the coat of COPI vesicles (Tu et al. 2008, 2012). ERp44 fulfills at least two functions, firstly it works as an effector for quality control, and secondly, it serves as a retrieval adapter for thiol-assisted retention of misfolded or unmounted heavy and light IgM chains (Anelli et al. 2003). ERp44 is able to shuttle between the ER and the Golgi, whereby it performs its quality control cycle in a pH-driven fashion (Anelli et al. 2007; Cortini and Sitia 2010; Vavassori et al. 2013). In the ER with a pH of 7.1, ERp44 is located therein in a closed conformational shape and hence cannot interact with the cargos or the KDEL receptor. In the cis-Golgi with a pH of 6.7, the ERp44 can perform a conformational alteration that prefers the interaction with cargo proteins containing unpaired cysteines and exposing a non-burried KDEL motif (Cortini and Sitia 2010; Vavassori et al. 2013; Sannino et al. 2014). Linked to the KDEL receptor, the ERp44 protein is transported backwards to the ER, where its cargo protein is released and the chaperone are prepared for additional quality control cycles.

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7.10.3 Regulatory Processes of Cargo Sorting and Impact on Trafficking by the Cargo One of the still unanswered questions in the protein sorting field is how the biogenesis of vesicles is regulated to adapt to the physiological requirement of each individual cell. Are vesicles made up locally solely upon the delivery of the cargo? Can the vesicles be designed in such a way that they are only specific for a certain cargo load? A central key to answering these questions is the comprehension of mechanistic effects of coat composition in relation to different cargo proteins. The clathrin system definitely involves large-scale movements by adapter complexes during cargo binding to initiate framework recruitment. It is yet to be seen whether a comparable localized activation arises in the COPI and COPII systems. Similarities in the structure of the COPI and clathrin-AP complex indicate that allosteric alterations probably govern the assembly of COPI (Dodonova et al. 2015); however, there are additional discoveries about how the various coat subcomplexes interact with one another before answering this question. Conversely, the COPII system seems to suffer from a lack of allosteric alterations in cargo binding, but the charge-related modification of coat construction can be accomplished instead by regulating coat stabilization. In this case, both envelope and accessory proteins can modify the GTP cycle of the envelope and thereby control the residence time of the envelope on the membrane. The peripheral ER protein Sec16 is in competition with Sec31 for its binding to Sec23/Sec24, which delays the enrollment of Sec31 and avoids the complete activation of the GTPase activity of Sar1 (Kung et al. 2012; Yorimitsu and Sato 2012). Mutations in Sec24 that cancel this process lead to the assumption that this process could be affected by the cargo loading on Sec24 (Kung et al. 2012), even though this has yet to be proven. Although much remains to be known about how charge can affect vesicle generation, there is ample evidence that vesicle production may not be fixed and static, but rather sufficiently flexible to meet different cellular demands.

7.10.4 Large Carriers Represent a Specialized ER Export Mechanism Both yeast and mammalian cells are able to secrete unusually large proteins, which hinder the efficient assembly of small, spherical vesicles for transport issues. Yeast Pma1 is a plentiful oligomeric protein from *1 MD, which has a specific need for the Sec24 paralogue Lst1/Sfb1, generating slightly bigger vesicles than those produced with Sec24 on its own (Shimoni et al. 2000; Miller et al. 2002). In the same way, GPI-APs depend on Lst1 for their highly efficient ER export; the binding of GPIAPs triggers the p24 complex for the targeted recruitment of Lst1 (Castillon et al. 2009, 2011; Manzano-Lopez et al. 2015). GPI-APs are likely to necessitate custom adjustments due to the nature of their asymmetric spread across the membrane, which probably tends to antagonize vesicle production (Copic et al. 2012). In both examples, the cargo itself, by recruiting a mantle component (Lst1) with the ability to produce larger vesicles, is responsible for entering a vesicle that can hold it. In mammalian cells, the trafficking of fibrillar procollagens causes a similar physical problem: the cargo protein is too large to be transported by a canonical COPII vesicle. Cargo transport therefore requires transmembrane accompanying factors such as MIA3/TANGO1 and cTAGE5, which themselves are not trapped in vesicles (Malhotra and Erlmann 2011, 2015). Similar to a traditional cargo receptor, TANGO1 can bind to collagen through its SH3 domain, which supports the packing of collagens into vesicles (Saito et al. 2009, 2011). It has recently been implied, however, that SH3 alone is not capable of conducting collagens in COPII vesicles. Rather, Hsp47 appears to serve as an anchor molecule between TANGO1 and several different types of collagens (Ishikawa et al. 2016). cTAGE5 represents a TANGO1-related protein that is able to interact with Sec12. Sec12 is the nucleotide exchange factor that can induce the assembly of the COPII coat through the attaching of

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GTP to Sar1. Through the local linkage of Sar1 activation to a certain cargo, TANGO1/cTAGE5 is expected to moderate the acquisition of adequate amounts of the COPII envelope to produce a large “megacarrier” that can carry procollagen. Besides the simple stimulation of COPII enrollment, TANGO1/cTAGE5 can also directly affect the coat geometry. Both proteins possess several interspersed polyproline motifs scattered across their cytoplasmic domains, which are attached to Sec23 (Ma and Goldberg 2016). By concurrently recruiting several Sec23/Sec24 dimers, TANGO1/cTAGE5 could place these mantle elements in a grid-like configuration similar to that encountered in COPII-coated tubular structures (Zanetti et al. 2013). Another special feature of the polyproline bond of Sec23 is that Sec31 also incorporates these interspersed motifs, which are able to battle for the bond with those of TANGO1/cTAGE5 (Ma and Goldberg 2016). The result is a model in which Sec31, when enrolling at the site of beginning vesicle formation, dislodges the “receptor” that provided the cargo acquired during local mantle formation. In this instance, the interactions between the cargo and the coating can determine both the geometry of the coating and the timing of the coating removal, which directly affects the final architecture of the substrate. Other regulatory layers also appear to be involved in the modulation of COPII envelope activity during procollagen export. For instance, the ubiquitination of Sec31 is necessary for collagen transport (Jin et al. 2012) and the alteration of the Sar1 GTPase activity through a TANGO1-associated protein sedlin seems to be additionally crucial (Venditti et al. 2012). How these factors could directly or indirectly impact the emergence of megacarriers is still to be clarified.

7.10.5 Cargo-Driven Signaling into the cis-Golgi A contribution of cargo to the direct encouragement of COPI vesicle production has been proposed by several unrelated pathways. The p24 protein family includes COPI binding cues that can modify the catalytic activity of the envelope, stabilize it possibly during assembly and thereby foster vesicle generation (Goldberg 2000). Analogous responses have been suggested for a family of proteins that circulate cyclically in yeast between ER and Golgi. In specific detail, the overexpression of Mst27/Mst28 represses the lethality linked to COPI mutants, which indicates that vesicle development can be encouraged by increasing COPI binding events (Sandmann et al. 2003). More precisely, the binding of a KDEL-containing ligand to the KDEL receptor triggers the oligomerization, as it has been seen through the fluorescence resonance energy transfer, and thereby triggers the COPI interaction with Golgi membranes (Majoul et al. 2001). How these multiple charge-driven effects are mechanistically handled, is not yet completely clarified. A second facet of cargo-driven traffic manipulation is proposed from recent research on Src-based cytoplasmic signaling events subsequent to the activation of the KDEL receptor (Pulvirenti et al. 2008). It is assumed that the binding of clients within the Golgi activates the kinases of the Src family, which themselves affect membrane transport. How this signal path could directly stimulate the COPI function needs to be studied more thoroughly, while more direct retardation testing would tend to predict a more general impact on Golgi traffic than a specific retrograde inducement (Bard et al. 2003).

7.10.6 How Is the Process of Sorting Dynamically Regulated? With many new advances in the structural properties of coating assembly and the interaction between cargo and coating, a deeper understanding of the mechanisms of protein sorting is being achieved. However, understanding the cellular control of these processes is not yet complete, and an appreciation of cargo–coat allostery (or other types of regulation) will be the key to how trafficking

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strategies can be adapted to suit specific cellular conditions. Further progress results from the investigation of non-canonical traffic, such as procollagen secretion, an area where the plasticity of established systems and the operational implications of new intrinsic features are only now starting to be estimated. However, new examples of the different sorting procedures are needed to develop the field continuously in a forward direction. Actually, an example of “non-canonical” transport involves the reactivation of an old model: the non-selective bulk flow. Several cargoes leave the ER with no concentration in vesicles or interference with the vesicle envelope, albeit probably remaining in a COPII-dependent mode (Wieland et al. 1987). Since the established cargo receptors transport just a fraction of the secretome, the stochastic transport via the bulk flow has to be reassessed as an important mechanism of cargo movement. In fact, a newly performed quantification of bulk flow in mammalian cells using a novel stable inert and quickly folding tracer indicates that there is a substantial flow of fluid and protein through the secretory route in a non-selective mode (Thor et al. 2009). Whether this also applies to any other cell types and organisms has yet to be clarified. Equally, it has not been verified whether the bulk flow of fluid and membrane takes place in retrograde vesicles, and it may be technically challenging to distinguish them from anterograde incidents. In view of bulk flow export, it is unclear how incompletely folded proteins and ER-residents could be impeded from being trapped in vesicles and needs to be further characterized. Similar to the problem of selectivity, it is difficult to understand how the folding of proteins impacts the ER–Golgi trafficking, in terms of both the signal-driven sorting and the bulk flow. This is an important issue, as emerging cargo proteins that are exported prematurely lose access to ER folding and dismantling machines. In essence, cargo adapters and receptors in both the ER and the Golgi are capable of directly assessing transport reliability. ER receptors can distinguish between correctly folded proteins and those proteins, which require the action of chaperones. Golgi receptors can bind misfolded cargo proteins in order to retrieve them back toward the ER. While in practice this model may be adequate for some misfolded proteins, it is improbable that it can be extended to all proteins. First, some ER export signals represent simple peptides that are probably not altered when a protein folds on the luminal part of the membrane. Probably even more relevant and enigmatic is the finding that a large number of imperfectly folded and resident proteins are in fact capable of being transported (Hong et al. 1996; Jenness et al. 1997; Spear and Ng 2003; Liu et al. 2006; Kincaid and Cooper 2007; Ashok and Hegde 2009; Wang and Ng 2010). Thereby, it seems to be more accurate to propose that the functional ER export signals can even coexist with ERAD and retention signals and moreover, the forward trafficking is dynamically governed by numerous competing interplays. Exposed to stress, the ER can utilize sorting signals as an ER detoxification mechanism (Kawaguchi et al. 2010; Satpute-Krishnan et al. 2014). However, similar questions still remain to be answered as to how misfolded proteins can be identified within the Golgi, and to what extent retrieval maintains the steady-state ER retention requires to be quantitively measured. Various misfolding evoked diseases seek to reveal the interaction between these pathways that stem from defects in quality control mechanisms during the sorting of proteins at the ER–Golgi interaction side.

7.11

Ribosomes

The ribosomes represent the universal translating machines, which convert each mRNAs into the encoded proteins. The genetic material of eukaryotic cells is enclosed by a nuclear envelope that serves as a barrier for the free travelling of proteins and nucleic acids between the nucleus and its surrounding cytoplasm. The resulting physical separation of transcription and translation enables the

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cells to govern the level of control of gene expression in eukaryotes, which is not possible in prokaryotes. Moreover, this also needs to the presence of an efficient transport system to selectively shuttle macromolecules between these two compartments. Such a specific system must be able to distinguish between a large number of structurally and functionally different molecules and can also react to different growth and environmental circumstances. The biosynthesis and transport of ribosomes in a rapidly growing cell, such as the baker’s yeast S. cerevisiae, represents a fundamental paradigm for the extent of the difficulties of the nucleocytoplasmic transport system. Every individual mRNA coding for a ribosomal protein first has to be transported into the cytoplasm where it is translated. The resultant ribosomal proteins then enter the cell nucleus and subsequently the nucleolus, where they combine with newly synthesized ribosomal RNA (rRNA). The preribosomes created in this way then pass through a series of sophisticated changes before they are re-exported into the cytoplasm to fulfill their function in translation. In a rapidly growing culture of yeast that double their ribosomal content every 1.5 h, each cell must import about 150,000 ribosomal proteins per minute via the nuclear envelope and it needs to simultaneously export about 4000 ribosomal subunits per minute (Warner 1999). There were tremendous improvements in the methodology that enabled us to follow a sketchy, sequential route throughout the constituents and mechanisms of ribosome biogenesis (Kressler et al. 1999). This route starts with the manufacture of ribosomal proteins, proceeds through their entry into the nucleus and their combination with the nascent rRNA in the nucleolus, and leads back toward the nuclear envelope during the not yet defined steps of prereibosomal subunit maturation. But so far, the road has had a blatant pothole: the proteins that specifically convey the export of the ribosomal subunit have been difficult to grasp. An idea of this process was gathered by examining the process of ribosome biogenesis in yeast (Ho et al. 2000). Such a finding has highlighted actors in this export process and potentially connected the last stages of maturation of the ribosomal subunit with its nuclear export, underlining the continuum of the ribosome’s life cycle starting from its birth and maturation in the nucleus to the functional involvement in translation in the cytoplasm.

7.11.1 Ribosomes Are Transported via the Nuclear Transport System Transportation of proteins and RNA between the nucleus and the cytoplasm is facilitated by soluble transport factors that are able to interact with cargos and drive them through multiple pores within the nuclear envelope. In the pores, there are enormous macromolecular arrangements, referred to as nuclear pore complexes, which function as gate guards of the nucleus (Wente 2000). The nuclear pore complexes permit the free diffusion of small molecules such as water and ions, although they shut out all macromolecules beyond the diffusion limit (*9 nm), with the exception of those bearing specific nucleocytoplasmic target sequences. Thus, proteins bearing a nuclear localization sequence (NLS) can be intrinsically transported across the nuclear pore complex, whereas macromolecules intended to be transferred out of the nucleus host nuclear export sequences (NES). These signals can be detected through karyopherins (sometimes abbreviated as kaps), which are synonymously referred to as importins, transportins and exportins (Mattaj and Englmeier 1998). Karyopherins are able to connect to the import or export signals of their cargoes, facilitate the connection of these cargoes toward the nuclear pore complex and consequently chaperone the cargoes via the pores into the nucleus. The GTPase Ran is responsible for the transfer of cargo between the nucleus and the cytoplasm. Ran is kept through the aid of cofactors in an GTP-bound state inside the nucleus and maintained in an GDPbound state throughout the cytoplasm. This distribution establishes an energy gradient over the nuclear pore complex that drives the transport between the nucleus and the cytoplasm and can also be utilized by transport players to identify on what side of the nuclear envelope they are localized. Thus,

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the establishment of an import compound between a karyopherin and its cargo is stabilized in the cytoplasm by cytoplasmic Ran–GDP, whereas in the nucleoplasm, Ran–GTP stimulates the breakdown of this compound. On the contrary, the creation of an export compound in the nucleus becomes more stable by Ran-GTP, but once this compound enters the cytoplasm, the GTP on Ran is hydrolyzed and the compound decays. At this point, it is obvious that there are various transport routes that make effective usage of different related karyopherins. There are at least 14 structurally closely related karyopherins in yeast that are involved in the import and export of certain classes of molecules, and it is expected that this number will be considerably higher in metazoans. In this way, numerous diverse, but sometimes mutually overlapping and redundant transport pathways are merged in the nuclear pore complex. One of the most widely trafficked routes is the exporting of ribosomal subunits.

7.11.2 What Function Fulfills the Ribosome? In general, ribosomes are regarded as one of the most fundamental complexes that can be found in nearly all organisms. As offspring of an ancient RNA universe, they consist mainly of a catalytically active rRNA scaffold on which the about 80 proteins are built. It appears that the principal function of most proteins within the ribosome (at least for the large subunit of the bacterium Halarula marisomortui) lies in the three-dimensional structural fixation (Ban et al. 2000). But it is also probable that numerous proteins linked to the ribosome participate in guiding it through the numerous steps in its biogenesis, both in time and space (Aitchison and Rout 2000). Ribosomal proteins, just as other proteins, are generally produced in the cytoplasm. Despite the fact that most of them are small and therefore lie below the nominal cutoff point of the nuclear pore complex, they are still actively introduced by capping-driven processes. In yeast, Kap123p is the primary source of this, but it is obvious that the process is superfluous and other karyopherins can absorb a large part of the sludge in the absence of Kap123p (Rout et al. 1997). When introduced into the cell nucleus, ribosomal proteins combine with newly transcribed rRNA inside the cell nucleus to produce a pre-ribosomal particle. Due to the difficulty of distinguishing the mechanism of ribosome export from its biogenesis, understanding the complex nature of this process is crucial. In yeast, more than 60 trans-acting elements are required for proper biogenesis of ribosomes (Kressler et al. 1999). Most of these proteins and small RNA components are stored inside the nucleolus, although they are equally abundant in the nucleoplasm, the cytoplasm and to some extent even in the nuclear pore complex. However, the precise roles of many of these factors are elusive and some factors may even fulfill other functions within the nucleus, such as the splicing of intron-carrying pre-mRNAs. In line with this, it is uncertain as for when and where all ribosomal proteins attach themselves to the rRNA. Nevertheless, the assembly of the preribosomes is closely synchronized with rRNA maturation and rRNA transport and can be regarded as a succession of consecutive steps, from the sites of nucleolar rRNA transcription through the nucleoplasm to the nuclear pore complex and beyond (Kressler et al. 1999). Across all eukaryotic cells, three of the four rRNAs are transcribed as a single RNA polymerase I transcript that undergoes comprehensive splicing, trimming and other covalent alterations to generate the 18, 5.8, 25/28S rRNA species. The fourth, the 5S RNA, is also pronouncedly altered, albeit transcribed by RNA polymerase III and recruited individually into the mounting ribosome. First, the primary RNA polymerase I transcript combines with a variety of large and small subunit proteins to yield a pre-90S particle. The transcript is thereafter split to obtain precursors of the 60S and 40S subunits. These appear to be transferred separately into the cytoplasm. The pre-40S particle passes through a further cytoplasmic ripening of its 20S rRNA to obtain the fully mature 18S rRNA, while the rRNA within the 60S particle is expected to ripen shortly before export. Once inside the

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cytoplasm, the 40S subunit attaches to the 5′ end of the mRNA and scanning to the initiation codon, where this translation-initiation complex will be awaiting the 60S subunit to adhere and initiate translation. The 60S subunit has to complete a series of cytoplasmic maturation cycles prior to drive a translation. Such fine-tuning probably requires the addition of several proteins, including Rpl10p. The inclusion of the 60S subunit in the translation engine is also synchronized with the release of factors, including eIF2 from the initiation engine. In the lack of effective 80S ribosome assembly, eliciting, for example, an inadequate amount of functional 60S subunits, the 40S subunit jams, resulting in clustering of half-meters. These kinetic intermediates of polysomes with a blocked 40S opening compound are typical for 60S biogenesis disorders. The ribosome represents the factory of cells for the synthesis of proteins. With rates of protein synthesis reaching up to 20 amino acids per second and an accuracy of 99.99%, the exceptional catalytic power of the bacterial translation engine stimulated considerable development, reconstruction and reuse efforts for biochemical trials and new functionalities. Notwithstanding these endeavors, the full exploitation potential of the translation device for the production of bio-based products beyond natural boundaries continues to be untapped, and fundamental limitations of the chemistry that the active center of the RNA-based ribosome can perform are largely unidentified. Ribosomes constitute macromolecular units, which are the key locations of protein synthesis or translation within every cell. The principal chemical step in protein synthesis at ribosomes is peptidyl transfer, whereby the expanding or developing peptide is passed from one tRNA molecule to the attached amino acid on another tRNA. Amino acids are inserted into the expanding polypeptide on the ribosome following the codon sequence of an mRNA. The ribosome therefore contains binding locations for one mRNA and a minimum of two tRNAs. Ribosomes consist of two different subunits, the large and the small subunit, comprising a handful of rRNA molecules and a diverse number of ribosomal proteins. Several protein triggers trigger various steps of protein synthesis. The accuracy of the genetic code translation is crucial for the manufacture of functional proteins and the viability of the entire cell.

7.12

Ribosome Mechanics Sheds Light on the Mechanism

The fundamental characteristics of the ribosome mechanism are derived from coarse-grained simulations, involving the ratchet movement, the joint movement of critical bases in the decoding hub, and the displacement of the peptide tunnel lining that helps eject the synthesized peptide. Due to its huge size, the coarse grain contributes to simplifying and better comprehension of the mechanism. The findings reported here exploit coarse-grained elastic network modeling to identify the dynamics, and both RNAs and proteins are coarse-grained. To verify the results obtained so far, the well-known ratchet movements and the movements in the peptide tunnel and in the mRNA tunnel are examined. The movements of the casing of the peptide tunnel apparently support the ejection of the increasing peptide chain, and brackets at the ends of the mRNA tunnel consisting of three proteins assure that the mRNA is retained firmly throughout the decoding phase and plays an indispensable part for helicase activity at the entry point (Zimmermann et al. 2016). The input block can also aid in base identification to guarantee the appropriate choice of entering tRNA (Zimmermann et al. 2016). The total accuracy of the ribosome as a machine is noteworthy. The translation of the genetic code of DNA into protein compounds is the main activity performed by the ribosome. The ribosome is an amazingly complicated ribonucleoprotein construct that engages with a variety of distinct substrates and cofactors along the translation mechanism. Knowledge of the dynamics of the ribosome is crucial to a better grasp of its mechanism. Great efforts have been made to elicit the structural elements of the ribosome in its various shapes, the ribosome structures of

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different organisms and the binding sites of pharmaceutical drugs. All these investigations result in the conclusion that the ribosome is a very rugged molecular device with numerous mobile components. In fact, the number of different functional ribosomal conformations (states) needed throughout translation is completely undefined and, notwithstanding the significance of the ribosome for cell viability, it is possible that experiments may leave it imperfectly defined. Calculations can play a role by detecting supplementary modes. In fact, there are several approaches made to reveal more insights into the detailed mechanism of the ribosome’s work. Cryo-electron microscopy (cryo-EM) has helped to discover the distinct steps of the protein synthesis pathway (Agrawal et al. 1999; Frank et al. 1999; Frank and Agrawal 2000; Valle et al. 2002; VanLoock et al. 2000; Wriggers et al. 2000). Based on an analysis of 3-D cryo-EM instantaneous images of the functional state of the intact ribosome, in its different functional states, it was reported (Frank and Agrawal 2000) that ratchet-like rotations of the 30S subunit relative to the 50S subunit during translocation are very similar to the movements observed in the dynamics simulations (Fig. 7.8) (Wang et al. 2004). Although many specific details have been revealed, such as the sequential steps during the translocation, there are still many specificities that have not yet been fully uncovered (Agrawal et al. 2000; Noller and Baucom 2002). Other biophysical techniques, such as NMR and FRET, have delivered additional specific details (Blanchard et al. 2004a, b; Johnson 2004; Lynch et al. 2003; Woolhead et al. 2004; Cooperman and Goldman 2006; Vanzi et al. 2003, 2005; Wang et al. 2007). Experimental structure identification may yield the patterns of several states of ribosomal protein and RNA compounds equivalent to several distinct states with various bound ligands, fixed in several manners, in multiple surroundings, and so on, but overall, identifying all potential structural stages to capture all the intricacies of the ribosome mechanism is a tremendous challenge. Computation can be crucial in complementing the existing structural and functional insights with simulations that give further specifics on the dynamics of the ribosome. A comprehensive knowledge of the mechanism will only be gained by collecting dynamic properties from a wide range of various computer simulations. The movements of the ribosome are effectively modeled by atomic molecular dynamics simulations over extraordinarily long periods of time. However, these simulations sometimes do not give an elementary description of the mechanism. Various facets of the ribosomal dynamics and its components have been examined with a variety of computational approaches including fully atomic molecular dynamics (Sanbonmatsu 2006a, b, 2012, 2014; Sanbonmatsu and Joseph 2003;

Fig. 7.8 The ribosome 30S and 50S subunits of prokaryotes and 60S and 40S of eukaryotes

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Sanbonmatsu et al. 2005; Sanbonmatsu and Tung 2006; Tung and Sanbonmatsu 2004), coarsegrained molecular dynamics (Trylska et al. 2005), normal mode analysis employing a coarse-grained potential (Wang et al. 2004, 2007; Kurkcuoglu et al. 2008, 2009a, b; Wang and Jernigan 2005), elastic framework models (Tama 2003; Tama et al. 2003) and subsequently the combination of experimental data and computational data (Mitra and Frank 2006). The focus here is on the mechanical properties of the ribosome in a coarse-grained formulation. The application of basic elastic network models is a suitable and an instructive tool for this task, which can deliver straightforward findings to enable the understanding of a number of mechanistic features of translation and the elongation of proteins. More in-depth examinations of a number of models with several included or excluded constituents are essential for the comprehension of ribosomal function. These simulations can supplement and improve the knowledge that can be acquired experimentally. The research into the dynamics of biological processes is the ideal basis for understanding the functional principles of the ribosome. For the description of harmonic and anharmonic movements of proteins and their assemblies, computer simulation techniques are often employed that make use of fully atomic empirical capabilities, such as molecular dynamics simulations (Levitt et al. 1985; McCammon and Harvey 1987; Tirion 1996; Kitao and Go 1999). The low-frequency movements in which large segments of proteins are involved, typically movements of entire domains, are frequently linked to their biological functionalities (Berendsen and Hayward 2000), such as the crossovers in proteins between the open and closed conformations (Kim et al. 2002a, b, 2003, 2005; Schuyler and Chirikjian 2005; Schuyler et al. 2009; Yang et al. 2005, 2007, 2009; Tama and Sanejouand 2001) and the ratchet-like movement of the ribosome in the course of protein synthesis (Frank et al. 2000). Nevertheless, with the aid of coarse-grained models, these domain motions are far better understood, particularly in systems considerably smaller than the ribosome. For exceptionally large-scale systems such as the ribosome, complete atomic simulations not only require enormous computational resources, but are also extremely complicated to evaluate and interpret when it comes to pinpoint the major steps of the mechanisms. Besides the atomic MD, difficulties also exist when it comes to ensuring sufficiently long trajectories for a profound investigation of their overall movements, once again due to the high requirements on computing capabilities. The coarse-grained scale models allow easier, more immediate evaluations with a higher degree of confidence that all potentially relevant movements are fully taken into account. At this point, a review of what has been achieved so far from previous ribosome simulations, but also some new findings that demonstrate how the ribosome pins the incoming mRNA, which is needed for its helicase activity to unroll the mRNA (Takyar et al. 2005) and to establish a binding site that can perceive the bases that are primed for delivery in front of the anticodon of the tRNA. Consequently, there are exists multiple diverse structures of the ribosome and its compounds within the Protein Data Bank (PDB) (Berman et al. 2000). The results described belong are based on the T. thermophilus structure, which has been the first completely identified structure (referred to pdb id::4V42). As with other bacterial ribosomes, it is made up of two large subunits, the 30S and the 50S, which rotate relative to each other in their dominant ratcheting movement at successive stages of synthesis.

7.12.1 Physical Models Can Be Employed for Elastic Networks Elastic network models, such as the Gaussian network model (GNM) (Bahar et al. 1997), can be employed for scalar descriptions of motions and the anisotropic network model (ANM) (Atilgan et al. 2001) can be utilized to describe the directions of motion. It is assumed that the initial structure has

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the least energy and the energy is in Gaussian format, which increases with the square of the displacements from the initial structure, as described in (7.1):   Z
c   

 1 U T DRi  DRj exp UðtÞ ¼ tr DRðtÞ CDRðtÞ DRi  DRj ¼ dDR ZN kT 2 3kT  1  C ij ¼ c

ð7:1Þ

where U stands for the energy, c represents the spring constant and DRi indicates the displacement of the point i within the structure. The input to the movements can be broken down into normal modes, defined by the eigenvalues and the eigenvectors of this matrix C−1. This is an excellent way to measure the harmonic movements of a structure around its original structure. It considers the geometric restrictions of the construction and provides a mechanical template. The stunning thing to learn from many implementations of these techniques is simply that it is the type of structure that governs the movements (Lu and Ma 2005; Doruker and Jernigan 2003; Doruker et al. 2002a, b). Generally, elastic network models are employed on coarse-grained constructions, and the findings demonstrate the mechanical movements of a system by using a uniform potential (all springs are assumed as being identical) for the interfering node couples in the system (Bahar and Rader 2005; Ma 2005; Jernigan et al. 2008). Coarse grain is usually carried out as one point per amino acid for proteins, but models with lower resolution, i.e., with several residues per node, have also yielded acceptable performance for the identification of low-frequency collective modes (Doruker et al. 2002a, b, 2005; Kurkcuoglu et al. 2004), while the general structure of the molecule is maintained. Other alternative tasks for coarse-grained normal mode investigations are the building block approach (Tama et al. 2000) and the minimalist network model (Lu and Ma 2008). It is easy to calculate the correlations of the variations between any two points in the structure DRi and DRj by inverting the Kirchhoff matrix, which is then defined by inserting a one in the matrix for points in the structure which are very close to one another, using a limiting distance to this end, and 0 elsewhere in the matrix, where the diagonal is assumed to be the negative sum of the other items in a particular line. The Kirchhoff matrix of the contacts C is concerned, and normally, a spring is positioned between close points in the framework which are defined by a limiting gap (Wang et al. 2004; Atilgan et al. 2001). For this example, by using Ca atoms for the proteins and two atoms for each nucleotide, the P and O4* atoms, coarsely engraved, the cutoff spacing is 15 Å. In specific detail, the dimensions of the square matrices which are to be diagonalized are 16,266 for the 30S subunit and 29,238 for the 70S subunit. Alternative approaches to coarse graining have been tested, and these show very small differences. One of the great advantages of these methods is their insensitivity to detail. A simulation of the complete ribosome provides the calculated correlations between the various parts of the ribosome microstructure and helps to decipher its underlying principles. The eigenvectors define the direction of movement of the individual items in the structure. The available results comprise the collective findings for the vectors of the first 100 movement modes of the ribosome. The correlation function can be defined as a cosine function, and therefore, a value of one indicates a perfect correlation in the direction of motion of the two points, and zero indicates either no correlation or a tangential movement. The prevalence of the ratchet movement is evident in the strong anticorrelation between the 30S and 50S subunits. All minor separate constituents—the tRNAs and the mRNAs—are more strongly positively associated. The tRNAs have almost zero correlation to the 30S and 50S subunits, whereas the mRNA appears to be strongly linked to the 30S and uncorrelated with the 50S subunits.

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7.12.2 Ratchet Motion Within a Complex of Three tRNAs and One mRNA In general, the most dominant movement seen is the ratchet movement between the large and small subunits of the ribosome. In the simulations, the first 10 normal modes, which depict the most collective movements of the ribosome, display the ratchet-like rotation of the two subunits, the head rotation of the small subunit and various types of anticorrelated movements between the large rods. Moreover, the ratchet-like rotation associated with certain countermovements of the L1 and L7/L12 stalks was found in various diverse normal modes. The large 50S substructures are architecturally intertwined with its proteins, and it possesses twice the amount of protein contacts with the rRNA than the proteins in the small subunit. The only exceptions are the L1 and L7/L12 rods, which are elongated without rRNA connections and which were experimentally challenging to image. The small subunit possesses pronounced internal domain flexibility (Liljas 2004), whereas the large subunit seems to be stiffer and more rigid. It has been shown that the global ratchet movement can be reproduced reliable through the employment of elastic network models (Wang et al. 2004) and has been revealed whether the tRNAs and mRNA are detectable (Kurkcuoglu et al. 2008). There is an inherent feature of the ribosomal structure that it facilitates its ratchet movement, and this capacity does not rely on the availability of the tRNAs or mRNA.

7.12.3 Complex Motions at an Early Decoding Stage The modeling of the ribosome architecture within the complex with three tRNAs and mRNAs demonstrated the computational power of the elastic network models. Beyond the ratchet movement, several other characteristic insights are detected that are discussed in the following. The examination of the movements in the decoding region provides numerous interesting features of the reading apparatus of the ribosome codon (Kurkcuoglu et al. 2008). For instance, there is a larger mobility of the anticodon stem loop (ASL) of the A-tRNA compared to the movements of P-tRNA and mRNA. The A site of mRNA is actually far more mobile than the P site, which is in good agreement with the experimental B factors, and also with the overall capacity of the A site to house a variety of proteins and RNAs, as well as the demand to maintain the P site more rigid in order to guarantee fidelity through strong recognition between codon and anticodon. In the simulation model, a high-resolution atomic region (Kurkcuoglu et al. 2009a, b; Kurkcuoglu et al. 2004) for the codon and anticodon regions of the mRNA and tRNAs at the A and P sites and the decoding site A1492, A1493 of the 16S rRNA in the small subunit have been incorporated. The ratchetic rotation and countermovements of the L1 and L7/L12 rods were monitored concurrently with the movement of the atoms in the decoding center and the movement of the codon/anticodon region. The lacking proteins on the L7/L12 handle in the crystalline structure were incorporated to provide density for this area of the elastic mesh. The elongated protein L9 on the large subunit is erased in the structure to eliminate its massive motility, that otherwise leads to a dominance of the calculated movements with the lowest frequency. Via 3/4 the movements from the entirely coarse-grained model are maintained in this mixed coarse-grained model; thus, it is an approximate but still offers the most essential subtleties. For the accumulative initial 10 modes, the mean squared fluctuations were computed for several levels of mixed coarse grain sizes. The third longest-running normal mode of this mixed coarsegrained ribosome model most closely resembles the ratchet-like movement of the subunits which is seen in cryo-EM. The side chains of A1492, A1493 and the A-tRNA show greater mobility in the slower modes than in the P-tRNA and mRNA. The analyses indicated that G34 of the A-tRNA and U43 of the mRNA exhibit smaller relationships with the decoding center (Kurkcuoglu et al. 2008).

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The benefit of incorporating these pieces as atoms is to monitor meaningful features of the movements closest related to its functional operations. For instance, the bases A1492 and A1493 are much more agile than their backbone, something not evident in a coarse-grained model.

7.12.4 The mRNA Helicase Function of the Ribosome The residues Arg131, Arg132, and Lys135 on S3, and additionally the residues Arg47 and Arg50 on S4 possess all helicase activity (Takyar et al. 2005), they are closely located nearby the mRNA and they coordinately perform their motion with the associated mRNA in the culminating ten slowest movements. S3 is able to perform an individual movement nearby the mRNA during the overall movement, in such a fashion that each of its side chains Glu161, Gln162 and Arg164 and the Arg49 of S4 is sufficiently close to the mRNA to be capable of interfering with it. These residues and any new contacts established in the alternative configurations could play an essential part in the activity of the ribosome helicase (Kurkcuoglu et al. 2008).

7.12.4.1 Protein S5 Orientates the mRNA for the Translation Variations in the mean squared distance between S5 residues and nucleotide A27 indicate that the entire ribosomal S5 protein stays nearer to the mRNA than the S3 and S4 proteins. These findings point to the fact that the close positional alignment of S5 with the mRNA seems to be functionally essential to guide the strand for the accurate reading of the frames at the A site, a fact confirmed by earlier work (Kurkcuoglu et al. 2008). However, the opening and closing of the other two proteins surrounding the mRNA may be examined since these interactions are less persistent in dynamics simulations. 7.12.4.2 Proteins S3, S4, and S5 Serve as Gate for the mRNA A new result has revealed how the three proteins S3, S4 and S5 seem to govern the entrance of the mRNA. It has been observed that protein S3 possesses the largest mobility. In a normal mode in which it operates, the movements interact alternately with these three proteins to clip the mRNA where it joins the ribosome, with a related group of three proteins functioning as the clip when the mRNA leaves the ribosome. This movement is aligned with the head swivel movement of the 30S subunit. In the case of any of the tracked entries, 5′ end will be opened and the end of 3′ will be shut down alternately, or vice versa. The mRNA at the entry of the tunnel for the mRNA is then confined by the proteins S3, S4 and S5, that restrict the mRNA in a bracket-like manner. The S3 movements are mainly accountable for this restriction. When the entrance to the mRNA tunnel is shut, the geometry of the opening alters. It gets thinner on one side, indicating the possibility that it possibly even scans the base of the ingoing mRNA. This capture of the mRNA bases may be structurally transferred to align the selection of the tRNA and assure that the anticodon of the tRNA is matched to this codon of the mRNA. Allostery in the structure facilitates the transfer of mRNA sensing to the incoming tRNA to guarantee the correspondence between codon and anticodon. That is a novel feature and necessitates additional examination of allostery within the ribosome. It is an excellent example of how certain ribosomal movements can result in structures that perceive certain regions and transmit knowledge across long ranges, in which case to assure proofreading of the ingoing tRNA anticodon. At the outlet of the mRNA tube the ribosomal proteins S7, S11 and S18 are responsible for the restriction of the mRNA. This lock may refer to the fact that the mRNA is decoded sideways by the rigid retention of the mRNA itself. The mRNA interfaces with the 3′ end of the 16S RNA and creates the Shine– Dalgarno complex for the initiation step. The 3′ end of the 16S RNA can act as a “hook” to roll up the

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mRNA and ease its release. (Kurkcuoglu et al. 2008). It is probable that this change between 2 modes —open input—closed output and closed input—open output—all in all has a major impact on the overall performance of the decoding accuracy. The structure of ribosomes is seemingly under strong scrutiny and guides the dynamics of mRNA entering, translocating and exiting.

7.12.4.3 The Dynamics of tRNAs Inside the Ribosome The ribosome experiences large movements to induce the translocation of tRNAs and mRNAs. The movements experimentally experienced in translocation are intrinsically governed by the ribosomal structure, causing the ratchet movement to propel these interior elements on an almost linear track. Normal mode results also demonstrate that the mobility of A- and P-tRNAs enhances without the presence of E-tRNA. The dynamics of the E-tRNA are also significantly influenced by the lack of the ribosomal protein L1. According to the simulations, the L1 arm deforms and seems to adhere to the EtRNA, which probably helps to detach the E-tRNA out of the ribosome. 7.12.4.4 The Ribosomal Peptide Tunnel Exhibits Collective Dynamical Properties The collective dynamic al properties of the polypeptide exit tunnel have been additionally examined. Low-frequency fluctuations have been found in three specific regions in the tunnel, such as the entrance, the neck and the exit. The linings of these three specific regions exhibit different types of motions. In broad terms, it can be said that the lining of the entry region travels in the output direction to aid in the elimination of the peptide, although the neck itself has much more complex movements. The exit area has much larger movements that rotate with the exit vector as an axis of rotation. It is noteworthy that the generally conserved prolongations of the ribosomal proteins L4 and L22, which are located at the narrowest entrance area of the tunnel, usually perform opening and closing movements, which can play an essential role in the emerging polypeptide gate mechanism to guarantee the stiffness of the reaction center in the closed state; this movement largely conforms to the circular peristaltic movements shown at this place (Kurkcuoglu et al. 2009a, b). 7.12.4.5 Intrinsic Movements of the tRNAs Are Closedly Linked to Their Movements Inside the Ribosome The movements apparent from various tRNA structures are closely similar to those of simulations of the elastic network (Zimmermann and Jernigan 2014). The first three major normal mode movements of tRNA have been revealed (Bahar et al. 1997). For every structure, it is demonstrated how it travels according to its conformational modification route. The first mode features the acceptor arm with large movements and may be necessary to ease amino acid adaptation as the tRNA entries the ribosome to seek its proper binding position. The second mode contains a bending of the structure resembling the movements experienced during the ratchet movement and constituting Noller’s hybrid state (Ramrath et al. 2013; Spiegel et al. 2007). The third movement mode entails unrolling and stretching the anticodon stalk loop, and this can obviously be instrumental in achieving the exact matching between codon and anticodon. Moreover, the movements of the tRNA at all three sites A, P and E are almost identical after their rigid body movements were eliminated (Wang and Jernigan 2005). The tRNA elbows interfere with areas of the 50S subunit and suffer large conformational alterations, while the anticodon ribbon loops interact in accordance with the 30S. The bending of the tRNA during the ratchet movement is fundamental and intrinsic to the structure of tRNA. These curved tRNA structural mid-products seem to be identical to the hybrid states of Noller (Ramrath et al. 2013; Spiegel et al. 2007). The first two movements of the independent tRNAs referred to are probably affected when the tRNA initially joins the ribosome to assure the proper alignment of the acceptor arm and the anticodon of the tRNA. Once the tRNA is completely bound in the ribosome, these pieces cannot move,

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and actually, these pieces of tRNA are considered the stiffest pieces of the tRNAs when located in the ribosome. Since the charged amino acid ends of A- and P-tRNAs are attached close to the peptidyltransferase center in the center of the 70S complex, these are the least fluctuating regions in the tRNAs. The anticodon is also fixed very rigidly to the codon of the mRNA. There are many findings from simulations that refer directly to single features of the mechanisms of the ribosome. The ratchet movement is extremely powerful and hard to undermine, and even after removal of all proteins (Kurkcuoglu et al. 2008). The dominant movement guarantees that the active compounds—the tRNAs and the mRNA—pass through the ribosome correctly. Many results obtained during the simulations are in agreement with what is commonly accepted knowledge of the ribosome and its movements, including the hybrid bent state of the tRNAs and the peristaltic movement inside the tunnel of the peptides. Moreover, even additional conformations have been obtained by simulations that may provide novel functional roles, such as the partial shut down of the mRNA tunnel opening in order to detect the bases of the codon and utilize this gained results for choosing the suitable tRNA, which still needs to be experimentally confirmed. In fact, these novel distinct interactions of possible functional impact that have been revealed through simulations need to be verified by employing mutagenesis. In general, the high level of precision in these precise allosteric interactions inside the ribosome delivers a high fidelity of entire translation process. The ribosome represents a complex machinery, and hence, coarse-grained simulations seem to uncover specific features of its mechanism The famous ratchet movement occurring between the two large subunits is the principal movement, and this is seen in the simulations where the t-RNAs constitute the hybrid state The compounds lining the mRNA tunnel can pin the mRNA into its position through their performance of distinct movements The lining of the peptide tunnel contains regions with significantly varying movements that support the emergence of the resulting peptide Coarse-grained dynamics models seem to be useful approaches for figuring out the molecular mechanistic insights of the largest molecular structures.

7.13

ER Mechanics

The ER represents a single organelle in eukaryotic cells that covers the entire cell and fulfills a wide variety of functions inside the cell. Employing a set of fixed and living cells (human MRC5 lung cells) in diffraction limited and super-resolved fluorescence microscopy (STORM) experiments, the average persistence length of ER tubules was found to be 3.03 ± 0.24 lm. The removal of the branched network intersections from the analysis resulted in a modest elevation of the average persistence length to 4.71 ± 0.14 lm and imparts a moderate dependence of the tubule persistence length on the length scale. The average tubulus radius has been sown to be 44.1 ± 3.2 nm. The bending rigidity of the membranes of ER tubules has been revealed to be 10.9 ± 1.2 kT and 17.0 ± 1.3 kT without any connecting points. The dynamic nature of ER tubules has been investigated in live cells, and it has turned out that the ER tubules exhibited a behavior of semi-flexible fibere that are tensioned (Georgiades et al. 2017). Most ER tubules undergo equilibrium transverse fluctuations, when they experience tension, whereas a minority of them performed active super-diffusive movements powered by motor proteins. In addition, the cells are able to actively modulate the dynamic properties of the ER in a carefully coordinated manner, which presumably influences its multiple functionalities.

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The ER represents a remarkably intricate organelle that occurs in eukaryotic cells and is surrounded by an uninterrupted membrane that traverses the complete cell. Two diverse morphological areas are discernable in the ER; an area that encircles the nucleus and seems flatter and leaf-like, and an area that straddles the peripheral areas in the cell, which comprises a pattern of tubules (Shibata et al. 2006, 2010), with incidental lamellar spots (Shemesh et al. 2014). Accordingly, the ER mesh cannot be regarded as being static over time, it is instead extremely dynamic, constantly experiencing redistributions, channel configurations, enlargements and fluctuations in their movement (Chen et al. 2013; Borgese et al. 2006; Wozniak et al. 2009). Being the biggest organelle in eukaryotic cells, the ER demonstrably participates in numerous functional processes of the cell, such as the synthesis of proteins, the monitoring of quality and the break-down of proteins (Sita and Braakman 2003), exchange of ions and lipids (Eden et al. 2016), the induction of the endosomal translocation (Raiborg et al. 2015) and the regulation of apoptotic events (Csordas et al. 2006). The progress made in imaging techniques and the consequent availability of higher resolution images has led to new insights into the structure of supposedly flat ER plates (Shibata et al. 2010; Shemesh et al. 2014). Combining high-resolution fluorescence imaging techniques for living and fixed cells, it was possible to detect ER tubules in areas previously considered ER leaflets by identifying gaps in the leaf-shaped peripheral ER (Nixon-Abell et al. 2016). Until now, these have been unexplored because of the limits of spatial and temporal resolution of the imaging procedures, and their presence has been verified by means of electron microscopy. Moreover, studies on the dynamic characteristics of the ER have been conducted by tracing three-way crossings between the tubules, emphasizing the dynamic oscillations of the ER tubules and the crossings along with its extremely flexible shape and morphology (Nixon-Abell et al. 2016; Valm et al. 2017). The polymer physics of semi-flexible fibers is today a comprehensive and well-established research area (Broedersz and MacKintosh 2014; Granek 1997). The standard calculations employing the equipartition theorem enable the determination of the persistence length (Lp) of semi-flexible fibers to be associated with the elasticity of the fibers. The persistence length (Lp) is defined as the length scale over which angular correlations in the tangent direction over the entire backbone are not corrected with the distance along the contour of the backbone. Hence, the persistence length provides a key property of every polymers and aids in the quantification of their rigidity. In reality, a polymer chain exhibits flexible properties when its length extends beyond its persistence length, while it acts as a rigid bar for lengths far smaller than its persistence length and as a semi-flexible chain on length scales of the same order of magnitude as its persistence length (Gittes et al. 1993, 16, 17). Supportive to the end-to-end distance and cross-sectional diameter, the persistence length represents one of the most important quantitative measurements for the description of the conformational state of a fiber. The majority of the cytoskeletal proteins within cells, such as actin with a Lp of approximately 10 ± 1 lm (Tassieri 2008) and microtubules with a Lp of approximately 5200 lm (Gittes et al. 1993), have been investigated precisely to uncover their impact on the cell mechanics, such as assessment of the energy deposited when bending a microtubular fiber during the deformation-driven reshaping of a cell. In this context, numerous biomacromolecules were investigated to gain an insight into the relationships between their conformations and their specific functional role, for example, the persistence length of DNA is *0.050 lm, having a slight reliance on the individual distinct sequence (Waigh 2014), and the persistence length can be exploited to comprehend the molecular interaction of DNA and histones. It is anticipated that the persistence length is potentially also a valuable geometric instrument for the characterization of ER tubules, helping to identify their function in various cell types which are characterized by distinct dedicated structures. There has been a little work undertaken on the geometry of the ER in cells derived from tobacco leaves obtained by confocal microscopy, but here polygons have been employed to comprehend the shape of the underlying network, and the flexibility of the tubules in this case has not been assessed (Lin et al. 2014).

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Recent advances with semi-flexible polymers broadened the theoretical framework for calculating

 2   ðtÞ , the transverse mean squared the effects of hydrodynamics on transverse displacements Dr? displacement as a function of time interval t of semi-flexible fiber without the implementation of

 2  3=4 

 2  1=2  and in tension-controlled regimes Dr? (Granek 1997). These ðtÞ  t ðt Þ  t tension Dr? kinds of theories for fiber dynamics coincide neatly with a number of experimental procedures such as fluorescence microscopy, photon correlation spectroscopy, bright field microscopy and a sequence of microrheology techniques (Jahnel et al. 2008; Carrick et al. 2005; Morse 1998). The newer theoretical concepts focus on the active movement of semi-flexible fibers linked to motor proteins (Ghosh and Gov 2014; Eisenstecken et al. 2016; Weber et al. 2015; Isele-Holder et al. 2015). While these theories are at a more early state of evolution than those for the passive fluctuations of fibers, they forecast a propelled super-diffusive movement of semi-flexible fibers on long-term time scales

 2  a  Dr? ðtÞ  t a [ 1 (Ghosh and Gov 2014; Eisenstecken et al. 2016; Weber et al. 2015; IseleHolder et al. 2015). A combination of live-cell imaging, conventional diffraction-limited epifluorescence microscopy and stochastic optical reconstruction microscopy (STORM) has been employed to analyze the mechanical properties of the ER tubules and the dynamic nature of single tubules. There is substantial evidence available to corroborate the idea that ER tubules act as hollow, tubular, semi-flexible fibers, with an average outer radius of 44.1 ± 3.2 nm, a persistence length, Lp, of 3.03 ± 0.24 lm (4.71 ± 0.14 lm on small length scales within the branching nodes), which is equivalent to a super soft membrane bending stiffness jmem, of 10.9 ± 1.2 kT (17.0 ± 1.3 kT on small length scales between the branching nodes), which is a characteristic feature of biological lipids in bilayer membranes (Boal 2012). In the past, data for the ER persistence length have never been provided, and hence, data have been recently revealed that lie between those of actin filaments, which means that are slightly smaller than actin Lp by a factor of 3 or 4 (Tassieri 2008) and DNA, which seems to be much larger by a factor of 60 (Philips 2013). Additionally, the values of the ER persistence length are usually one order of magnitude smaller than the overall size of the cell, in which they are determined. It is clear that the ER has an extremely soft microstructure and its input into the mechanics of whole cells is probably far less than that of microtubules, actins or intermediate filaments (Mofrad and Kamm 2006). There is ample proof of the active regulation of ER dynamics by motor proteins, and these active movements are anticipated to perform an essential task for ER functions (Nixon-Abell et al. 2016), such as the synthesis rate and the following alteration of proteins in a rocked reaction vessel model.

7.13.1 The Fit of the Fibers and Analysis of Fiber Mechanics Super-resolved and diffraction-limited pictures of the ER in fixed MRC5 cells as well as single videos of transiently transfected living MRC5 cells expressing EGFP-ER were adjusted using FiberApp, an open-source MATLAB toolbox capable of extracting structural data of fibrillar specimens in atomic force microscopy and fluorescence pictures (Usov and Mezzenga 2015). The contour of the ER tubules was adapted by the embedded algorithms based on active contour models, allowing the x– y position of the tubules to be computed. To determine the persistence length of the ER tubules, the contour length and the end-to-end distance of the active contour of ER tubules needs to be taken from the fluorescent images employing   the FiberApp (Valle et al. 2005; Cox et al. 2017). The mean squared end-to-end distance R2 can be described with (7.2):

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ER Mechanics

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  2 R ¼ 2Lp Lc  Lp 1  eLc =Lp

ð7:2Þ

where Lp is the persistence length and Lc represents the contour length (Mantelli et al. 2011). The end-to-end distance and the contour length can be determined using FiberApp, and subsequently, the persistence length can be extracted by fitting (7.2). As expected, the mean persistence length was 3.03 ± 0.24 lm. The mean squared end-to-end distance has been revealed to be a more robust for the calculation of the persistence lengths based on fluorescence microscopy images than the direct analysis of angular correlations, as it is less perturbed by the small length scale noise, such as for a semi-flexible fiber, where Lp * Lc, and most of the angular deflections is based on the largest   wavelength bending modes that also revealed R2 (Cox et al. 2017). Super-resolved images of fixated MRC5 cells were employed to analyze the outer radius R of the ER tubules, which is on average 44.1 ± 3.2 nm (Georgiades et al. 2017). Besides the persistence length and the cross-sectional radius, an additional crucial property of polymers is the bending rigidity, jpol, that determines how the applied bending moment, M, scales in line with the curvature d2wdr2, where r stands for the deflection and x represents the distance along the beam during the deformations (Mofrad and Kamm 2006), such as M ¼ jpol d2 w dr 2 . For a semiflexible fiber, the Lp can be correlated with the polymeric bending rigidity, when the equipartition theorem is used to analyze the bending energy, as given in (7.3): Lp ¼

jpol kB T

ð7:3Þ

where kBT represents the thermal energy (Gittes et al. 1993; Philips 2013). It is common knowledge that the ER fibers are no uniform rigid (solid) cylinders; hence, they are hollow cylinders, which wall is 3–5 nm thick (Nagle and Tristram-Nagle 2000). When the ER is seen as an elastic hollow cylinder with a radius R of negligible thickness, which represents a thin hollow cylinder approximation, (7.3) can be replaced. Hence, the persistence length (Lp) of (7.4) relies on the anisotropic elasticity of membranes (Derenyi et al. 2002): Lp ¼

2jmem pR kB T

ð7:4Þ

where jmem represents the membrane’s bending rigidity, which is a two-dimensional entity and is a proportionality constant between the free membrane energy and the averaged mean squared membrane curvature, R is the tubule radius and kBT represents the thermal energy. This particular approach has two advantages: Firstly, it provides the determination of the bending rigidity of the membranes that can be related to other experimental in the field of nanomechanics. Secondly, it has been extensively investigated with in vitro experiments, such as lipid nanotubules of variable radii (Yamamoto and Ichikawa 2012; Baroji et al. 2014; Tanaka-Takiguchi et al. 2012). When values for the persistence length and the ER tubules radii are inserted in (7.4), bending rigidity for the ER tubule membranes of 10.9 ± 1.2 kT (44.8 ± 4.9  10−21 J) can be obtained with the branch points included and without the branch point the value increases to 17.0 ± 1.3 kT (69.9 ± 5.3  10−21 J) (Georgiades et al. 2017). However, both values lie still inside the range of 3– 225 kT, which is usually detected for lipids in biological membranes (Boal 2012), and hence, they are in line with the predicted values. The membrane bending rigidity (jmem) can be determined from the fitted ER tubules and the data are log normal distributed. The bending rigidities, such as those with an average 10.9 ± 1.2 kT,

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imply very soft materials; however, they lie in the range of lipid bilayers. For comparison propose, the bending rigidity of a graphene bilayer is 1380 kT (Lindahl et al. 2012).

7.13.2 How Can ER Tubules Be Monitored? To characterize the ER tubular network in more detail, live-cell videos were utilized, where the ER motion can be tracked. In specific detail, the x–y coordinates of the ER tubules along a line perpendicularly to the ER tubule can be employed to finally determine the mean squared displacement (MSD) as a function of the lag time for all given tracks. The MSD is defined by (7.5): 

 E  D
Dr 2 ðsÞ ¼ xðt þ sÞ  xðtÞ2 þ ðyðt þ sÞ  yðtÞÞ2

t

ð7:5Þ

where t represents the time and s is referred to as lag time. Based on the tiny longitudinal movements of the fibers, the MSD seems to be a suitable

 2     ðsÞ of the fiber’s movements, such as Dr 2 ðsÞ ¼ approximation to the transverse MSD Dr?  2  D 2 E  2  Dr? ðsÞ þ Drk ðsÞ  Dr? ðsÞ (Broedersz and MacKintosh 2014). To characterize the dynamic   movement of the ER tubules, a power law of the form Dr 2 ðsÞ  sa can be fitted, which provides the determination of power exponent a values of the MSD curves. There are found two distinct populations of MSDs that are detected at longer time scales, such as those above 2 s: one population displays subdiffusive behavior, where a < 1 and the other population displays super-diffusive behavior, where the following applies: 1 < a < 2. The collective motion of the ER system on longer time scales, such as above 2 s, contributes to the increase of the exponent. The two subpopulations can be found due to their discrepancies in their power-law exponents at long time scales with individually averaged MSDs. A universal power law of s = 0.48 ± 0.02 determines the average MSD curve for the subdiffusive MSD subpopulation, whereas a power law of s = 0.58 ± 0.04 was characteristic for the super-diffusive subpopulation in the regime of short lag times, such as up to 2 s and a s = 1.53 ± 0.03 can be observed for the regime of longer lag times, such as those above 2 s. The ER represents a critical organelle in eukaryotic cells, since it regulates many basic cellular processes and its malfunction causes a variety of diseases (Roussel et al. 2013). Beyond that, the structural and mechanical characteristics of the ER are still not understood in detail. However, there are advances based on super-resolution microscopy and electron microscopy that enable researchers determine precisely previously unreachable small length scales to discover new insights in the ER’s tubular architectural network and the dynamical restructuring processes. An example is the finding that the ER sheets within the cell periphery are not positioned inside sheet-like morphologies, rather tightly packed tubules with triangular connections, so-called ER matrices, and moreover, morphological evidence has been found for the highly dynamic and transferable phenotype of these complex ER networks (Nixon-Abell et al. 2016; Valm et al. 2017). While the architecture, morphology and allocation of the ER grid system in a variety of cell lines are well defined, the mechanical properties of organelles have so far been poorly understood. The ER is endowed with a high number of proteins, ribosomes and active motor proteins that have the potential to modify its mechanical properties and dynamics (Lin et al. 2012; Voeltz et al. 2006; English et al. 2009). A persistence length of the ER tubules of 3.03 ± 0.24 lm was observed, both in living and fixed cells. The persistence lengths determined in the four unrelated experiments, such as live-cell imaging, diffraction-limited KLC3 antibody labeling and super-resolution imaging with varying labeling strategies, match well, eliminating artifacts from sample preparation. Besides the

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ER Mechanics

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persistence length of the ER tubules, we also calculated their average membrane bending stiffness, which amounted to 10.9 ± 1.2 kT. To verify the potential influence of ER contacts on the persistence length that is, an Lp dependent on the length scale caused by chemical heterogeneity, such as contact promoting proteins (Shemesh et al. 2014; Lin et al. 2012), the investigation was replicated for the stretches of the tubules between the connection points. A small incremental increase in the persistence length was noted of 4.71 ± 0.14 lm. The respective membrane bending stiffness obtained with (7.3) is also somewhat higher in the case of 17.0 ± 1.3 kT, which has to be anticipated, as the effects of branching proteins have been eliminated. The membrane bending stiffness value is linearly related to the persistence length (see 7.3), and vice versa to the tubule radius. This offers an obvious benefit of super-resolution imaging, as the detected radius of 44.1 ± 3.2 nm is a subdiffraction length scale for a standard optical microscope, but is far above the resolution limit of about 20 nm of the STORM microscope. The persistence lengths and membrane bending stiffnesses calculated from these experiments displayed a widespread distribution pointing to the wide spectrum of ER tubule rigidities available, which with wide fluctuations in both morphology and the constitution of the ER networks across the cell as a whole seems to be coherent. It has been proposed that the tubules’ curvature is guided through the interplay of two sets of proteins, the R-type and the S-type proteins. In specific detail, the R-type proteins, such as reticulons and DP1/Yop1p, support the assembly of tubules, while the S-type proteins, such as atlastins, Sey1p and Climp63, induce junctions within tubules and so-termed sheets (Shemesh et al. 2014; Lin et al. 2012). However, the full set of proteins that govern the morphology of the ER is not yet clearly known. In extension, the ER physically interfaces with a range of organelles, such as endosomes, and the movement of the ER has been associated directly with the microtubular cytoskeleton of the cell (Wozniak et al. 2009), both types of interference feeding into the general structure of the network (English et al. 2009). It is consequently deduced that the monitored distribution of persistence lengths and membrane bending stiffnesses is due to a complicated interference between the abovementioned elements, all of which disturb the ultrasoft tubular structures forming the ER. Besides the mechanical properties of the ER mesh, its dynamic characteristics were examined with live-cell imaging and cell tracking software. Two specific mean squared displacement (MSD) populations were revealed to be a function of delay time (s) for longer delay times, such as s > 2 s: the first one displays subdiffusive motion with 0 < a < 1 and the second one shows super-diffusive subballistic motion with 1 < a < 2. The subpopulation of super-diffusive subballistic tracked ER tubules may be impacted by the collective motion of the entire ER grid due to the overall movement of the cell or, more probably, the immediate outcome of an active mechanism fueled by motor proteins or the translocation of the tubule due to a temporary interplay with microtubules or vesicles (NixonAbell et al. 2016). By extrapolating tracks with super-diffusive movement in their MSDs at long times (>2 s), a universal power law was found with an exponent a of 0.48 ± 0.02 for any other tracks, which is consistent with the theoretical error forecast for semi-flexible fibers under tension that fixed at both ends, as provided in (7.6) (Granek 1997). Short-lived traces (